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Ayman El-Baz
Bioengineering Department, University of Louisville, Louisville, KY 40208, USA

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Journal article
Published: 29 August 2021 in Sensors
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Autism spectrum disorder (ASD) is a neurodegenerative disorder characterized by lingual and social disabilities. The autism diagnostic observation schedule is the current gold standard for ASD diagnosis. Developing objective computer aided technologies for ASD diagnosis with the utilization of brain imaging modalities and machine learning is one of main tracks in current studies to understand autism. Task-based fMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in response to certain tasks. It is believed to hold discriminant features for autism. A novel computer aided diagnosis (CAD) framework is proposed to classify 50 ASD and 50 typically developed toddlers with the adoption of CNN deep networks. The CAD system includes both local and global diagnosis in a response to speech task. Spatial dimensionality reduction with region of interest selection and clustering has been utilized. In addition, the proposed framework performs discriminant feature extraction with continuous wavelet transform. Local diagnosis on cingulate gyri, superior temporal gyrus, primary auditory cortex and angular gyrus achieves accuracies ranging between 71% and 80% with a four-fold cross validation technique. The fused global diagnosis achieves an accuracy of 86% with 82% sensitivity, 92% specificity. A brain map indicating ASD severity level for each brain area is created, which contributes to personalized diagnosis and treatment plans

ACS Style

Reem Haweel; Noha Seada; Said Ghoniemy; Norah Saleh Alghamdi; Ayman El-Baz. A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI. Sensors 2021, 21, 5822 .

AMA Style

Reem Haweel, Noha Seada, Said Ghoniemy, Norah Saleh Alghamdi, Ayman El-Baz. A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI. Sensors. 2021; 21 (17):5822.

Chicago/Turabian Style

Reem Haweel; Noha Seada; Said Ghoniemy; Norah Saleh Alghamdi; Ayman El-Baz. 2021. "A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI." Sensors 21, no. 17: 5822.

Journal article
Published: 14 August 2021 in Sensors
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A new segmentation technique is introduced for delineating the lung region in 3D computed tomography (CT) images. To accurately model the distribution of Hounsfield scale values within both chest and lung regions, a new probabilistic model is developed that depends on a linear combination of Gaussian (LCG). Moreover, we modified the conventional expectation-maximization (EM) algorithm to be run in a sequential way to estimate both the dominant Gaussian components (one for the lung region and one for the chest region) and the subdominant Gaussian components, which are used to refine the final estimated joint density. To estimate the marginal density from the mixed density, a modified k-means clustering approach is employed to classify the Gaussian subdominant components to determine which components belong properly to a lung and which components belong to a chest. The initial segmentation, based on the LCG-model, is then refined by the imposition of 3D morphological constraints based on a 3D Markov–Gibbs random field (MGRF) with analytically estimated potentials. The proposed approach was tested on CT data from 32 coronavirus disease 2019 (COVID-19) patients. Segmentation quality was quantitatively evaluated using four metrics: Dice similarity coefficient (DSC), overlap coefficient, 95th-percentile bidirectional Hausdorff distance (BHD), and absolute lung volume difference (ALVD), and it achieved 95.67±1.83%, 91.76±3.29%, 4.86±5.01, and 2.93±2.39, respectively. The reported results showed the capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches.

ACS Style

Ahmed Sharafeldeen; Mohamed Elsharkawy; Norah Saleh Alghamdi; Ahmed Soliman; Ayman El-Baz. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors 2021, 21, 5482 .

AMA Style

Ahmed Sharafeldeen, Mohamed Elsharkawy, Norah Saleh Alghamdi, Ahmed Soliman, Ayman El-Baz. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors. 2021; 21 (16):5482.

Chicago/Turabian Style

Ahmed Sharafeldeen; Mohamed Elsharkawy; Norah Saleh Alghamdi; Ahmed Soliman; Ayman El-Baz. 2021. "Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints." Sensors 21, no. 16: 5482.

Journal article
Published: 13 August 2021 in Sensors
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Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.

ACS Style

Sayed Haggag; Fahmi Khalifa; Hisham Abdeltawab; Ahmed Elnakib; Mohammed Ghazal; Mohamed A. Mohamed; Harpal Singh Sandhu; Norah Saleh Alghamdi; Ayman El-Baz. An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images. Sensors 2021, 21, 5457 .

AMA Style

Sayed Haggag, Fahmi Khalifa, Hisham Abdeltawab, Ahmed Elnakib, Mohammed Ghazal, Mohamed A. Mohamed, Harpal Singh Sandhu, Norah Saleh Alghamdi, Ayman El-Baz. An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images. Sensors. 2021; 21 (16):5457.

Chicago/Turabian Style

Sayed Haggag; Fahmi Khalifa; Hisham Abdeltawab; Ahmed Elnakib; Mohammed Ghazal; Mohamed A. Mohamed; Harpal Singh Sandhu; Norah Saleh Alghamdi; Ayman El-Baz. 2021. "An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images." Sensors 21, no. 16: 5457.

Journal article
Published: 11 August 2021 in Sensors
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Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

ACS Style

Fatma El-Zahraa A. El-Gamal; Mohammed Elmogy; Ali Mahmoud; Ahmed Shalaby; Andrew E. Switala; Mohammed Ghazal; Hassan Soliman; Ahmed Atwan; Norah Saleh Alghamdi; Gregory Neal Barnes; Ayman El-Baz. A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI). Sensors 2021, 21, 5416 .

AMA Style

Fatma El-Zahraa A. El-Gamal, Mohammed Elmogy, Ali Mahmoud, Ahmed Shalaby, Andrew E. Switala, Mohammed Ghazal, Hassan Soliman, Ahmed Atwan, Norah Saleh Alghamdi, Gregory Neal Barnes, Ayman El-Baz. A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI). Sensors. 2021; 21 (16):5416.

Chicago/Turabian Style

Fatma El-Zahraa A. El-Gamal; Mohammed Elmogy; Ali Mahmoud; Ahmed Shalaby; Andrew E. Switala; Mohammed Ghazal; Hassan Soliman; Ahmed Atwan; Norah Saleh Alghamdi; Gregory Neal Barnes; Ayman El-Baz. 2021. "A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)." Sensors 21, no. 16: 5416.

Journal article
Published: 20 July 2021 in Sensors
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Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

ACS Style

Mohamed Shehata; Ahmed Alksas; Rasha Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Razek; Norah Alghamdi; Ayman El-Baz. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors 2021, 21, 4928 .

AMA Style

Mohamed Shehata, Ahmed Alksas, Rasha Abouelkheir, Ahmed Elmahdy, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Hadil Abu Khalifeh, Reem Salim, Ahmed Abdel Razek, Norah Alghamdi, Ayman El-Baz. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. Sensors. 2021; 21 (14):4928.

Chicago/Turabian Style

Mohamed Shehata; Ahmed Alksas; Rasha Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Razek; Norah Alghamdi; Ayman El-Baz. 2021. "A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors." Sensors 21, no. 14: 4928.

Journal article
Published: 17 July 2021 in International Journal of Hydrogen Energy
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Alternative fuels and stocks like biomass or chemical and refinery waste, may potentially be used in gas turbines and industrial applications after gasification. Thus, understanding the role of hydrogen in these fuels is critical to the broader aim of utilising alternative fuels for power generation. In this work, the interaction between the flame and the flow field was studied in a quarl-stabilised swirl non-premixed flame burning CH4 and H2–enriched CH4. Simultaneous high-speed OH-PLIF/PIV imaging at 5 kHz was carried out on these flames to explore the flame-flow interaction. The instantaneous flow fields in the CH4 or CH4+H2 flames showed a small scale vortical structure near the shear layers, which were not apparent in the time-averaged flow fields. Increasing H2% in the fuel jet was observed to dampen the velocity fluctuations. The fuel composition affected the spatial location of the reaction zone; in the CH4 flames, the axial position of the reaction zone is seen to track the relatively large-magnitude axial velocity fluctuations while remaining in locally low-speed regions of the flow. In contrast, in H2-enriched flames, where the flame is more robust, the reaction zone was able to survive longer, in terms of axial distance, in the vicinity of high swirling jet velocity, with less sensitivity to velocity fluctuations. With increasing the H2%, the reaction zone steadily leaves the IRZ towards the swirling jet flow and localised between its outer and inner vortices. This acts as a stabilisation factor where the internal vortices convect hot product towards the fresh mixture. Moreover, the flame curvatures, the vorticity and compressive strain fields interactions with the reaction zone are presented and discussed. This article outlines results that yield more in-depth insight into hydrogen-enriched hydrocarbon non-premixed swirling flames' combustion, which is essential to accelerate the fuel switching from hydrocarbons to hydrogen.

ACS Style

Ayman M. Elbaz; Ossama Mannaa; William L. Roberts. Flame flow field interaction in non-premixed CH4/H2 swirling flames. International Journal of Hydrogen Energy 2021, 46, 30494 -30509.

AMA Style

Ayman M. Elbaz, Ossama Mannaa, William L. Roberts. Flame flow field interaction in non-premixed CH4/H2 swirling flames. International Journal of Hydrogen Energy. 2021; 46 (59):30494-30509.

Chicago/Turabian Style

Ayman M. Elbaz; Ossama Mannaa; William L. Roberts. 2021. "Flame flow field interaction in non-premixed CH4/H2 swirling flames." International Journal of Hydrogen Energy 46, no. 59: 30494-30509.

Journal article
Published: 15 July 2021 in IEEE Access
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Autism spectrum disorder (ASD) is a neuro-developmental disorder associated with impairments in social and lingual abilities. Failure in language development is variable in the ASD population and follows a wide spectrum. The autism diagnostic observation schedule (ADOS) is the current gold standard for diagnosing plus expert clinical judgment. Currently, studies aim to develop objective computer-aided technologies to diagnose autism with brain imaging modalities and machine learning. Task-based fMRI is a discriminating image modality that measures the functional activation of the brain. Computer-aided diagnosis systems aim to classify autistic subjects against typically developed peers despite the fact that autism is defined over a wide spectrum. Here, we propose a novel computer-aided grading framework in infants and toddlers (between 12 and 40 months) dependent on the analysis of brain activation in response to a speech experiment. First, brain activation responses are analyzed for 157 autistic subjects divided into three groups of: 92 mild, 32 moderate,and 33 severe as defined by ADOS calibrated severity score. Increased hypoactivation of the superior temporal cortex, angular gyrus, primary auditory cortex and cingulate gyri is exhibited with increasing autism spectrum severity. Less lateralization is also present when activation of the left hemisphere regions is recorded. Second, only these region of interest (ROI) areas are included for further local and global feature extraction in our ASD grading system. A comprehensive, two-stage system is developed using different classifiers. Four-fold cross-validation is adopted for testing. The first stage discriminates between moderate and the other two groups with an accuracy of 0.83 (sensitivity = 0.73, specificity = 0.83). Subsequently, a second stage classifies subjects as mild or severe autism with an accuracy of 0.81 (sensitivity = 0.81, specificity = 0.76). Finally, two validation techniques of synthesizing for oversampling and e of multiple random training and testing sets were adopted. The validation results proved the robustness of the proposed framework for an early computer-aided grading system to place subjects on the autism spectrum.

ACS Style

Reem Haweel; Ahmed M. Shalaby; Ali H. Mahmoud; Mohammed Ghazal; Noha Seada; Said Ghoniemy; Manuel Casanova; Gregory N. Barnes; Ayman El-Baz. A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study. IEEE Access 2021, 9, 100570 -100582.

AMA Style

Reem Haweel, Ahmed M. Shalaby, Ali H. Mahmoud, Mohammed Ghazal, Noha Seada, Said Ghoniemy, Manuel Casanova, Gregory N. Barnes, Ayman El-Baz. A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study. IEEE Access. 2021; 9 ():100570-100582.

Chicago/Turabian Style

Reem Haweel; Ahmed M. Shalaby; Ali H. Mahmoud; Mohammed Ghazal; Noha Seada; Said Ghoniemy; Manuel Casanova; Gregory N. Barnes; Ayman El-Baz. 2021. "A Novel Grading System for Autism Severity Level Using Task-Based Functional MRI: A Response to Speech Study." IEEE Access 9, no. : 100570-100582.

Journal article
Published: 08 June 2021 in Scientific Reports
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The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.

ACS Style

Mohamed Elsharkawy; Ahmed Sharafeldeen; Fatma Taher; Ahmed Shalaby; Ahmed Soliman; Ali Mahmoud; Mohammed Ghazal; Ashraf Khalil; Norah Saleh Alghamdi; Ahmed Abdel Khalek Abdel Razek; Eman Alnaghy; Moumen T. El-Melegy; Harpal Singh Sandhu; Guruprasad A. Giridharan; Ayman El-Baz. Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images. Scientific Reports 2021, 11, 1 -11.

AMA Style

Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz. Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images. Scientific Reports. 2021; 11 (1):1-11.

Chicago/Turabian Style

Mohamed Elsharkawy; Ahmed Sharafeldeen; Fatma Taher; Ahmed Shalaby; Ahmed Soliman; Ali Mahmoud; Mohammed Ghazal; Ashraf Khalil; Norah Saleh Alghamdi; Ahmed Abdel Khalek Abdel Razek; Eman Alnaghy; Moumen T. El-Melegy; Harpal Singh Sandhu; Guruprasad A. Giridharan; Ayman El-Baz. 2021. "Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images." Scientific Reports 11, no. 1: 1-11.

Journal article
Published: 04 June 2021 in Sensors
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Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.

ACS Style

Ahmed Naglah; Fahmi Khalifa; Reem Khaled; Ahmed Abdel Razek; Mohammad Ghazal; Guruprasad Giridharan; Ayman El-Baz. Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN. Sensors 2021, 21, 3878 .

AMA Style

Ahmed Naglah, Fahmi Khalifa, Reem Khaled, Ahmed Abdel Razek, Mohammad Ghazal, Guruprasad Giridharan, Ayman El-Baz. Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN. Sensors. 2021; 21 (11):3878.

Chicago/Turabian Style

Ahmed Naglah; Fahmi Khalifa; Reem Khaled; Ahmed Abdel Razek; Mohammad Ghazal; Guruprasad Giridharan; Ayman El-Baz. 2021. "Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN." Sensors 21, no. 11: 3878.

Journal article
Published: 25 May 2021 in Sensors
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Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

ACS Style

Islam Abdelmaksoud; Ahmed Shalaby; Ali Mahmoud; Mohammed Elmogy; Ahmed Aboelfetouh; Mohamed Abou El-Ghar; Moumen El-Melegy; Norah Alghamdi; Ayman El-Baz. Precise Identification of Prostate Cancer from DWI Using Transfer Learning. Sensors 2021, 21, 3664 .

AMA Style

Islam Abdelmaksoud, Ahmed Shalaby, Ali Mahmoud, Mohammed Elmogy, Ahmed Aboelfetouh, Mohamed Abou El-Ghar, Moumen El-Melegy, Norah Alghamdi, Ayman El-Baz. Precise Identification of Prostate Cancer from DWI Using Transfer Learning. Sensors. 2021; 21 (11):3664.

Chicago/Turabian Style

Islam Abdelmaksoud; Ahmed Shalaby; Ali Mahmoud; Mohammed Elmogy; Ahmed Aboelfetouh; Mohamed Abou El-Ghar; Moumen El-Melegy; Norah Alghamdi; Ayman El-Baz. 2021. "Precise Identification of Prostate Cancer from DWI Using Transfer Learning." Sensors 21, no. 11: 3664.

Journal article
Published: 01 May 2021 in Applied Sciences
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The optical coherence tomography angiography (OCTA) is a noninvasive imaging technology which aims at imaging blood vessels in retina by studying decorrelation signals between multiple sequential OCT B-scans captured in the same cross section. Obtaining various vascular plexuses including deep and superficial choriocapillaris, is possible, which helps in understanding the ischemic processes that affect different retina layers. OCTA is a safe imaging modality that does not use dye. OCTA is also fast as it can capture high-resolution images in just seconds. Additionally, it is used in the assessment of structure and blood flow. OCTA provides anatomic details in addition to the vascular flow data. These details are important in understanding the tissue perfusion, specifically, in the absence of apparent morphological change. Using these anatomical details along with perfusion data, OCTA could be used in predicting several ophthalmic diseases. In this paper, we review the OCTA techniques and their ability to detect and diagnose several retinal vascular and optical nerve diseases, such as diabetic retinopathy (DR), anterior ischemic optic neuropathy (AION), age-related macular degeneration (AMD), glaucoma, retinal artery occlusion and retinal vein occlusion. Then, we discuss the main features and disadvantages of using OCTA as a retinal imaging method.

ACS Style

Fatma Taher; Heba Kandil; Hatem Mahmoud; Ali Mahmoud; Ahmed Shalaby; Mohammed Ghazal; Marah Alhalabi; Harpal Sandhu; Ayman El-Baz. A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography. Applied Sciences 2021, 11, 4158 .

AMA Style

Fatma Taher, Heba Kandil, Hatem Mahmoud, Ali Mahmoud, Ahmed Shalaby, Mohammed Ghazal, Marah Alhalabi, Harpal Sandhu, Ayman El-Baz. A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography. Applied Sciences. 2021; 11 (9):4158.

Chicago/Turabian Style

Fatma Taher; Heba Kandil; Hatem Mahmoud; Ali Mahmoud; Ahmed Shalaby; Mohammed Ghazal; Marah Alhalabi; Harpal Sandhu; Ayman El-Baz. 2021. "A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography." Applied Sciences 11, no. 9: 4158.

Journal article
Published: 28 April 2021 in Applied Sciences
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Blood pressure (BP) changes with age are widespread, and systemic high blood pressure (HBP) is a serious factor in developing strokes and cognitive impairment. A non-invasive methodology to detect changes in human brain’s vasculature using Magnetic Resonance Angiography (MRA) data and correlation of cerebrovascular changes to mean arterial pressure (MAP) is presented. MRA data and systemic blood pressure measurements were gathered from patients (n = 15, M = 8, F = 7, Age = 49.2 ± 7.3 years) over 700 days (an initial visit and then a follow-up period of 2 years with a final visit.). A novel segmentation algorithm was developed to delineate brain blood vessels from surrounding tissue. Vascular probability distribution function (PDF) was calculated from segmentation data to correlate the temporal changes in cerebral vasculature to MAP calculated from systemic BP measurements. A 3D reconstruction of the cerebral vasculature was performed using a growing tree model. Segmentation results recorded 99.9% specificity and 99.7% sensitivity in identifying and delineating the brain’s vascular tree. The PDFs had a statistically significant correlation to MAP changes below the circle of Willis (p-value = 0.0007). This non-invasive methodology could be used to detect alterations in the cerebrovascular system by analyzing MRA images, which would assist clinicians in optimizing medical treatment plans of HBP.

ACS Style

Fatma Taher; Heba Kandil; Yitzhak Gebru; Ali Mahmoud; Ahmed Shalaby; Shady El-Mashad; Ayman El-Baz. A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure. Applied Sciences 2021, 11, 4022 .

AMA Style

Fatma Taher, Heba Kandil, Yitzhak Gebru, Ali Mahmoud, Ahmed Shalaby, Shady El-Mashad, Ayman El-Baz. A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure. Applied Sciences. 2021; 11 (9):4022.

Chicago/Turabian Style

Fatma Taher; Heba Kandil; Yitzhak Gebru; Ali Mahmoud; Ahmed Shalaby; Shady El-Mashad; Ayman El-Baz. 2021. "A Novel MRA-Based Framework for Segmenting the Cerebrovascular System and Correlating Cerebral Vascular Changes to Mean Arterial Pressure." Applied Sciences 11, no. 9: 4022.

Review
Published: 07 April 2021 in Sensors
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Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

ACS Style

Sarah Ayyad; Mohamed Shehata; Ahmed Shalaby; Mohamed Abou El-Ghar; Mohammed Ghazal; Moumen El-Melegy; Nahla Abdel-Hamid; Labib Labib; H. Ali; Ayman El-Baz. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors 2021, 21, 2586 .

AMA Style

Sarah Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Nahla Abdel-Hamid, Labib Labib, H. Ali, Ayman El-Baz. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors. 2021; 21 (8):2586.

Chicago/Turabian Style

Sarah Ayyad; Mohamed Shehata; Ahmed Shalaby; Mohamed Abou El-Ghar; Mohammed Ghazal; Moumen El-Melegy; Nahla Abdel-Hamid; Labib Labib; H. Ali; Ayman El-Baz. 2021. "Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey." Sensors 21, no. 8: 2586.

Journal article
Published: 31 March 2021 in Computerized Medical Imaging and Graphics
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Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors’ geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).

ACS Style

K. Hammouda; F. Khalifa; A. Soliman; M. Ghazal; M. Abou El-Ghar; M.A. Badawy; H.E. Darwish; A. Khelifi; A. El-Baz. A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging. Computerized Medical Imaging and Graphics 2021, 90, 101911 .

AMA Style

K. Hammouda, F. Khalifa, A. Soliman, M. Ghazal, M. Abou El-Ghar, M.A. Badawy, H.E. Darwish, A. Khelifi, A. El-Baz. A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging. Computerized Medical Imaging and Graphics. 2021; 90 ():101911.

Chicago/Turabian Style

K. Hammouda; F. Khalifa; A. Soliman; M. Ghazal; M. Abou El-Ghar; M.A. Badawy; H.E. Darwish; A. Khelifi; A. El-Baz. 2021. "A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging." Computerized Medical Imaging and Graphics 90, no. : 101911.

Journal article
Published: 28 March 2021 in Sensors
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Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

ACS Style

Mohamed Shaban; Reem Salim; Hadil Abu Khalifeh; Adel Khelifi; Ahmed Shalaby; Shady El-Mashad; Ali Mahmoud; Mohammed Ghazal; Ayman El-Baz. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors 2021, 21, 2351 .

AMA Style

Mohamed Shaban, Reem Salim, Hadil Abu Khalifeh, Adel Khelifi, Ahmed Shalaby, Shady El-Mashad, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors. 2021; 21 (7):2351.

Chicago/Turabian Style

Mohamed Shaban; Reem Salim; Hadil Abu Khalifeh; Adel Khelifi; Ahmed Shalaby; Shady El-Mashad; Ali Mahmoud; Mohammed Ghazal; Ayman El-Baz. 2021. "A Deep-Learning Framework for the Detection of Oil Spills from SAR Data." Sensors 21, no. 7: 2351.

Journal article
Published: 16 January 2021 in Journal of Engineering for Gas Turbines and Power
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Heavy fuel oil (HFO) is an economical fuel alternative for power generation as its low production cost and high energy density. However, its incomplete combustion induced by the presence of long-chain petroleum molecules in the fuel results in high levels of emissions. Here, we investigate the influence of the swirl flow on the combustion and emissions of a spray HFO swirling flame. To this end, HFO is sprayed into a hot swirling air, using an air-blast nozzle. The flame blowout limits are tested under different swirl flows. An investigation of the in-flame temperature fields, gaseous emissions including CO, CO2, O2, NOX, SOX, UHC (Unburned Hydrocarbon) and solid particles in the form of cenospheres are used to quantify the performance of the HFO combustion. The influence of the HFO swirling flame is tested under different conditions of global equivalence ratio, swirling number, and tangential and axial airflow rates. A comparison of two different flame regimes that fuel-jet dominate flame and air-driven vortex flows are investigated and compared in various swirling flow conditions. The results show that the tangent air is the primary factor for preheating and evaporating the fuel, thus defining the flame operating regimes.

ACS Style

Xinyan Pei; Ayman.M Elhagrasy; Long Jiang; Kamal M. Alahmadi; Saumitra Saxena; William Roberts. Heavy Fuel Oil Combustion Characteristics Evaluation in Various Swirling Flow Conditions. Journal of Engineering for Gas Turbines and Power 2021, 1 .

AMA Style

Xinyan Pei, Ayman.M Elhagrasy, Long Jiang, Kamal M. Alahmadi, Saumitra Saxena, William Roberts. Heavy Fuel Oil Combustion Characteristics Evaluation in Various Swirling Flow Conditions. Journal of Engineering for Gas Turbines and Power. 2021; ():1.

Chicago/Turabian Style

Xinyan Pei; Ayman.M Elhagrasy; Long Jiang; Kamal M. Alahmadi; Saumitra Saxena; William Roberts. 2021. "Heavy Fuel Oil Combustion Characteristics Evaluation in Various Swirling Flow Conditions." Journal of Engineering for Gas Turbines and Power , no. : 1.

Research article
Published: 15 January 2021 in Medical Physics
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Purpose Accurate segmentation of retinal layers of the eye in 3‐D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient‐specific retinal atlas, as well as an appearance model for 3D OCT data. Methods To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula mid‐area) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of co‐registered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula “foveal area”, the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until the a 3D OCT scan of the patient was segmented. Results This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95‐% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state‐of‐the‐art 3D OCT approaches. Conclusions The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state‐of‐the‐art 3D OCT approaches.

ACS Style

Ahmed A. Sleman; Ahmed Soliman; Mohamed Elsharkawy; Guruprasad Giridharan; Mohammed Ghazal; Harpal Sandhu; Shlomit Schaal; Robert Keynton; Adel Elmaghraby; Ayman El‐Baz. A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images. Medical Physics 2021, 48, 1584 -1595.

AMA Style

Ahmed A. Sleman, Ahmed Soliman, Mohamed Elsharkawy, Guruprasad Giridharan, Mohammed Ghazal, Harpal Sandhu, Shlomit Schaal, Robert Keynton, Adel Elmaghraby, Ayman El‐Baz. A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images. Medical Physics. 2021; 48 (4):1584-1595.

Chicago/Turabian Style

Ahmed A. Sleman; Ahmed Soliman; Mohamed Elsharkawy; Guruprasad Giridharan; Mohammed Ghazal; Harpal Sandhu; Shlomit Schaal; Robert Keynton; Adel Elmaghraby; Ayman El‐Baz. 2021. "A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images." Medical Physics 48, no. 4: 1584-1595.

Research article
Published: 30 December 2020 in Medical Physics
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Purpose Task‐based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN) based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. Methods To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard‐Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Secondly, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K‐means clustering technique is performed on such significant brain areas. Informative blood oxygen level‐dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. Results Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using 10‐fold cross validation (with sensitivity =84%, specificity = 76%) Conclusion The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

ACS Style

Reem Haweel; Ahmed Shalaby; Ali Mahmoud; Noha Seada; Said Ghoniemy; Mohammed Ghazal; Manuel F. Casanova; Gregory N. Barnes; Ayman El‐Baz. A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI. Medical Physics 2020, 48, 2315 -2326.

AMA Style

Reem Haweel, Ahmed Shalaby, Ali Mahmoud, Noha Seada, Said Ghoniemy, Mohammed Ghazal, Manuel F. Casanova, Gregory N. Barnes, Ayman El‐Baz. A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI. Medical Physics. 2020; 48 (5):2315-2326.

Chicago/Turabian Style

Reem Haweel; Ahmed Shalaby; Ali Mahmoud; Noha Seada; Said Ghoniemy; Mohammed Ghazal; Manuel F. Casanova; Gregory N. Barnes; Ayman El‐Baz. 2020. "A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI." Medical Physics 48, no. 5: 2315-2326.

Journal article
Published: 17 November 2020 in IEEE Access
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Alzheimer’s disease (AD) is a neurodegenerative condition that affects the central nervous system and represents 60% to 70% of all dementia cases. Due to an increased aging population, the number of patients diagnosed with AD is expected to exceed 131 million worldwide by 2050. The disease is characterized by various clinical symptoms and pathological features that define three main sequential decline stages, namely, early/mild, intermediate/moderate and late/severe stages. Although it is considered irreversible, early diagnosis of AD is highly desirable to help preserve cognitive function. However, early diagnosis is difficult due to different factors, including the patient-specific development of AD. The main contribution of the proposed work is to present a personalized (i.e., local/brain regional) computer-aided diagnosis (CAD) system for early diagnosis of AD from two perspectives, functional and structural to assist diagnosis. In other words, the proposed system uniquely yields local/regional diagnosis by combining 11C PiB positron emission tomography (11C PiB PET), which provides functional diagnosis, with structural magnetic resonance imaging (sMRI), which provides structural diagnosis. To the best of our knowledge, this is the first work to combine sMRI and the 11C PiB PET for local/regional early diagnosis of AD. The system processes the two modalities through a number of steps: pre-processing, brain labeling (parcellation), feature extraction, and diagnosis. A local/regional diagnosis is presented for each modality separately, followed by the final global diagnosis obtained by integrating the results from the two modalities. Evaluation of the proposed system shows average results of 97:5%, 100%, and 96:77% for accuracy, specificity, and sensitivity, respectively. With further development, it is envisioned that this system could contribute to the early diagnosis of AD in the clinical setting.

ACS Style

Fatma El-Zahraa A. El-Gamal; Mohammed M. Elmogy; Ashraf Khalil; Mohammed Ghazal; Jawad Yousaf; Xiaolu Qiu; Hassan H. Soliman; Ahmed Atwan; Hermann B. Frieboes; Gregory Neal Barnes; Ayman S. El-Baz. Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis. IEEE Access 2020, 8, 218982 -218996.

AMA Style

Fatma El-Zahraa A. El-Gamal, Mohammed M. Elmogy, Ashraf Khalil, Mohammed Ghazal, Jawad Yousaf, Xiaolu Qiu, Hassan H. Soliman, Ahmed Atwan, Hermann B. Frieboes, Gregory Neal Barnes, Ayman S. El-Baz. Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis. IEEE Access. 2020; 8 (99):218982-218996.

Chicago/Turabian Style

Fatma El-Zahraa A. El-Gamal; Mohammed M. Elmogy; Ashraf Khalil; Mohammed Ghazal; Jawad Yousaf; Xiaolu Qiu; Hassan H. Soliman; Ahmed Atwan; Hermann B. Frieboes; Gregory Neal Barnes; Ayman S. El-Baz. 2020. "Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis." IEEE Access 8, no. 99: 218982-218996.

Journal article
Published: 12 November 2020 in Medical Image Analysis
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Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

ACS Style

Omar Dekhil; Ahmed Shalaby; Ahmed Soliman; Ali Mahmoud; Maiying Kong; Gregory Barnes; Adel Elmaghraby; Ayman El-Baz. Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns. Medical Image Analysis 2020, 68, 101899 .

AMA Style

Omar Dekhil, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Maiying Kong, Gregory Barnes, Adel Elmaghraby, Ayman El-Baz. Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns. Medical Image Analysis. 2020; 68 ():101899.

Chicago/Turabian Style

Omar Dekhil; Ahmed Shalaby; Ahmed Soliman; Ali Mahmoud; Maiying Kong; Gregory Barnes; Adel Elmaghraby; Ayman El-Baz. 2020. "Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns." Medical Image Analysis 68, no. : 101899.