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Dr. Shuihua Wang
University of Leicester

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Research article
Published: 20 August 2021 in International Journal of Intelligent Systems
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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

ACS Style

Xiang Yu; Qinghua Zhou; Shuihua Wang; Yu‐Dong Zhang. A systematic survey of deep learning in breast cancer. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Xiang Yu, Qinghua Zhou, Shuihua Wang, Yu‐Dong Zhang. A systematic survey of deep learning in breast cancer. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Xiang Yu; Qinghua Zhou; Shuihua Wang; Yu‐Dong Zhang. 2021. "A systematic survey of deep learning in breast cancer." International Journal of Intelligent Systems , no. : 1.

Short communication
Published: 14 July 2021 in Pattern Recognition Letters
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COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day. This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap. The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%. Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.

ACS Style

Yu-Dong Zhang; Zheng Zhang; Xin Zhang; Shui-Hua Wang. MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray. Pattern Recognition Letters 2021, 150, 8 -16.

AMA Style

Yu-Dong Zhang, Zheng Zhang, Xin Zhang, Shui-Hua Wang. MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray. Pattern Recognition Letters. 2021; 150 ():8-16.

Chicago/Turabian Style

Yu-Dong Zhang; Zheng Zhang; Xin Zhang; Shui-Hua Wang. 2021. "MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray." Pattern Recognition Letters 150, no. : 8-16.

Short review
Published: 10 July 2021 in Information Fusion
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Due to the proliferation of biomedical imaging modalities, such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc., massive amounts of data are generated on a daily basis. While massive biomedical data sets yield more information about pathologies, they also present new challenges of how to fully explore the data. Data fusion methods are a step forward towards a better understanding of data by bringing multiple data observations together to increase the consistency of the information. However, data generation is merely the first step, and there are many other factors involved in the fusion process like noise, missing data, data scarcity, and high dimensionality. In this paper, an overview of the advances in data preprocessing in biomedical data fusion is provided, along with insights stemming from new developments in the field.

ACS Style

Shuihua Wang; M. Emre Celebi; Yu-Dong Zhang; Xiang Yu; Siyuan Lu; Xujing Yao; Qinghua Zhou; Martínez-García Miguel; Yingli Tian; Juan M Gorriz; Ivan Tyukin. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. Information Fusion 2021, 76, 376 -421.

AMA Style

Shuihua Wang, M. Emre Celebi, Yu-Dong Zhang, Xiang Yu, Siyuan Lu, Xujing Yao, Qinghua Zhou, Martínez-García Miguel, Yingli Tian, Juan M Gorriz, Ivan Tyukin. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. Information Fusion. 2021; 76 ():376-421.

Chicago/Turabian Style

Shuihua Wang; M. Emre Celebi; Yu-Dong Zhang; Xiang Yu; Siyuan Lu; Xujing Yao; Qinghua Zhou; Martínez-García Miguel; Yingli Tian; Juan M Gorriz; Ivan Tyukin. 2021. "Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects." Information Fusion 76, no. : 376-421.

Journal article
Published: 01 July 2021 in Arabian Journal for Science and Engineering
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In the area of computer vision (CV), action recognition is a hot topic of research nowadays due to famous applications, which include human–machine interaction, robotics, visual surveillance, video analysis, etc. Many techniques are presented in the literature by researchers of CV, but still they faced a lot of challenges such as complexity in the background, variation in the camera view point and movement of humans. A new method is proposed in this work for action recognition. The proposed method is based on the shape and deep learning features fusion. Two-steps-based method is executed— human extraction to action recognition. In the first step, first, humans are extracted by simple learning process. In this process, HOG features are extracted from few selected datasets such as INRIA, CAVIAR, Weizmann and KTH. Then, we need to select the robust features using entropy-controlled LSVM maximization and performed detection. Second, geometric features are extracted from detected regions and parallel deep learning features are extracted from original video frame. However, the extracted deep learning features are high in dimension and some are not relevant, so it is essential to remove irrelevant features before fusion. For this purpose, a new feature reduction technique is presented named as entropy-controlled geometric mean . Through this technique, we can select the robust deep learning features and remove the irrelevant of them. Finally, both types of features (selected deep learning and original geometric) are fused by proposed parallel conditional entropy approach. The obtained feature vector is classified by a cubic multi-class SVM. Six datasets (i.e., IXMAS, KTH, Weizmann, UCF Sports, UT Interaction and WVU) are used for the experimental process and achieved an average accuracy of above 98.00%. The detailed statistical analysis and comparison with existing techniques show the the effectiveness of proposed method .

ACS Style

Muhammad Attique Khan; Yu-Dong Zhang; Majed Alhusseni; Seifedine Kadry; Shui-Hua Wang; Tanzila Saba; Tassawar Iqbal. A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition. Arabian Journal for Science and Engineering 2021, 1 -16.

AMA Style

Muhammad Attique Khan, Yu-Dong Zhang, Majed Alhusseni, Seifedine Kadry, Shui-Hua Wang, Tanzila Saba, Tassawar Iqbal. A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition. Arabian Journal for Science and Engineering. 2021; ():1-16.

Chicago/Turabian Style

Muhammad Attique Khan; Yu-Dong Zhang; Majed Alhusseni; Seifedine Kadry; Shui-Hua Wang; Tanzila Saba; Tassawar Iqbal. 2021. "A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition." Arabian Journal for Science and Engineering , no. : 1-16.

Journal article
Published: 05 June 2021 in Applied Soft Computing
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Cerebral microbleed (CMB) is a type of biomarker, which is related to cerebrovascular diseases. In this paper, a novel computer aided diagnosis method for CMB detection was presented. Firstly, sliding neighborhood algorithm was used to generate CMB and non-CMB samples from brain susceptibility weighted images. Then, a 15-layer proposed FeatureNet was trained for extracting features from the input samples. Afterwards, structure after the first fully connected layer in FeatureNet was replaced by three randomized neural networks for classification: Schmidt neural network, random vector functional-link net, and extreme learning machine, and the weights and biases in early layers of FeatureNet were frozen during the training of those three classifiers. Finally, the output of the three classifiers was ensemble by majority voting mechanism to get better classification performance. In our experiment, five-fold cross validation was employed for evaluation. Results revealed that our FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, which outperformed several existing state-of-the-art approaches. The proposed FeatureNet-EN model could provide accurate CMB detection, and thus reduce death tolls. Impact Statement — We propose a 15-layer FeatureNet to detect cerebral microbleed (CMB). We propose three FeatureNet variants: FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM. We use ensemble learning to combine three FeatureNet variants, and generate a FeatureNet-EN. The proposed FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, better than state-of-the-art methods. This method could provide accurate CMB detection, and thus reduce death tolls.

ACS Style

Si-Yuan Lu; Deepak Ranjan Nayak; Shui-Hua Wang; Yu-Dong Zhang. A cerebral microbleed diagnosis method via FeatureNet and ensembled randomized neural networks. Applied Soft Computing 2021, 109, 107567 .

AMA Style

Si-Yuan Lu, Deepak Ranjan Nayak, Shui-Hua Wang, Yu-Dong Zhang. A cerebral microbleed diagnosis method via FeatureNet and ensembled randomized neural networks. Applied Soft Computing. 2021; 109 ():107567.

Chicago/Turabian Style

Si-Yuan Lu; Deepak Ranjan Nayak; Shui-Hua Wang; Yu-Dong Zhang. 2021. "A cerebral microbleed diagnosis method via FeatureNet and ensembled randomized neural networks." Applied Soft Computing 109, no. : 107567.

Review
Published: 22 May 2021 in Multimedia Tools and Applications
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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

ACS Style

Jian Wang; Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. A review on extreme learning machine. Multimedia Tools and Applications 2021, 1 -50.

AMA Style

Jian Wang, Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang. A review on extreme learning machine. Multimedia Tools and Applications. 2021; ():1-50.

Chicago/Turabian Style

Jian Wang; Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. 2021. "A review on extreme learning machine." Multimedia Tools and Applications , no. : 1-50.

Original research
Published: 19 April 2021 in Journal of Ambient Intelligence and Humanized Computing
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ACS Style

Zhengming Li; Zheng Zhang; Shuihua Wang; Ruijun Ma; Fangyuan Lei; Dan Xiang. Structured analysis dictionary learning based on discriminative Fisher pair. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -18.

AMA Style

Zhengming Li, Zheng Zhang, Shuihua Wang, Ruijun Ma, Fangyuan Lei, Dan Xiang. Structured analysis dictionary learning based on discriminative Fisher pair. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-18.

Chicago/Turabian Style

Zhengming Li; Zheng Zhang; Shuihua Wang; Ruijun Ma; Fangyuan Lei; Dan Xiang. 2021. "Structured analysis dictionary learning based on discriminative Fisher pair." Journal of Ambient Intelligence and Humanized Computing , no. : 1-18.

Original article
Published: 08 April 2021 in Expert Systems
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Aim Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth. Methods This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five‐layer stacked sparse autoencoder was used as the classifier. Results Ten runs on the test set showed our method achieved a micro‐averaged F1 score of 95.08% for an 18‐category fruit dataset. Conclusion Our method gives better micro‐averaged F1 score than 10 state‐of‐the‐art approaches.

ACS Style

Yu‐Dong Zhang; Suresh Chandra Satapathy; Shui‐Hua Wang. Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder. Expert Systems 2021, 1 .

AMA Style

Yu‐Dong Zhang, Suresh Chandra Satapathy, Shui‐Hua Wang. Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder. Expert Systems. 2021; ():1.

Chicago/Turabian Style

Yu‐Dong Zhang; Suresh Chandra Satapathy; Shui‐Hua Wang. 2021. "Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder." Expert Systems , no. : 1.

Neuroscience
Published: 01 April 2021 in Frontiers in Neuroscience
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Brain functional networks (BFNs) constructed via manifold regularization (MR) have emerged as a powerful tool in finding new biomarkers for brain disease diagnosis. However, they only describe the pair-wise relationship between two brain regions, and cannot describe the functional interaction between multiple brain regions, or the high-order relationship, well. To solve this issue, we propose a method to construct dynamic BFNs (DBFNs) via hyper-graph MR (HMR) and employ it to classify mild cognitive impairment (MCI) subjects. First, we construct DBFNs via Pearson’s correlation (PC) method and remodel the PC method as an optimization model. Then, we use k-nearest neighbor (KNN) algorithm to construct the hyper-graph and obtain the hyper-graph manifold regularizer based on the hyper-graph. We introduce the hyper-graph manifold regularizer and the L1-norm regularizer into the PC-based optimization model to optimize DBFNs and obtain the final sparse DBFNs (SDBFNs). Finally, we conduct classification experiments to classify MCI subjects from normal subjects to verify the effectiveness of our method. Experimental results show that the proposed method achieves better classification performance compared with other state-of-the-art methods, and the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under the curve (AUC) reach 82.4946 ± 0.2827%, 77.2473 ± 0.5747%, 87.7419 ± 0.2286%, and 0.9021 ± 0.0007, respectively. This method expands the MR method and DBFNs with more biological significance. It can effectively improve the classification performance of DBFNs for MCI, and has certain reference value for the research and auxiliary diagnosis of Alzheimer’s disease (AD).

ACS Style

Yixin Ji; Yutao Zhang; Haifeng Shi; Zhuqing Jiao; Shui-Hua Wang; Chuang Wang. Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification. Frontiers in Neuroscience 2021, 15, 1 .

AMA Style

Yixin Ji, Yutao Zhang, Haifeng Shi, Zhuqing Jiao, Shui-Hua Wang, Chuang Wang. Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification. Frontiers in Neuroscience. 2021; 15 ():1.

Chicago/Turabian Style

Yixin Ji; Yutao Zhang; Haifeng Shi; Zhuqing Jiao; Shui-Hua Wang; Chuang Wang. 2021. "Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification." Frontiers in Neuroscience 15, no. : 1.

Journal article
Published: 13 March 2021 in Journal of Ambient Intelligence and Humanized Computing
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We propose a novel graph rank-based average pooling neural network (GRAPNN) to detect secondary pulmonary tuberculosis patients via chest CT imaging. First, we propose a novel rank-based pooling neural network (RAPNN) to learn the individual image-level features from chest CT images. Second, we integrate the graph convolutional network (GCN), which learns relation-aware representation among the batch of chest CT images, to RAPNN. Third, we build a novel Graph RAPNN (GRAPNN) model based on the previous integration via k-means clustering and k-nearest neighbors’ algorithm. Besides, an improved data augmentation is utilized to handle overfitting problem. Grad-ACM is used to make this GRAPNN model explainable. This proposed GRAPNN method is compared with seven state-of-the-art algorithms. The results showed GRAPNN model yields the best performances with a sensitivity of 94.65%, a specificity of 95.12%, a precision of 95.17%, an accuracy of 94.88%, and an F1 score of 94.87%. Our GRAPNN is superior to other seven state-of-the-art approaches. The explainable mechanism in our method can identify the lesions of important lung parts (tuberculosis cavities and surrounding small lesions) for transparent decision.

ACS Style

Shui-Hua Wang; Vishnu Govindaraj; Juan Manuel Gorriz; Xin Zhang; Yu-Dong Zhang. Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -14.

AMA Style

Shui-Hua Wang, Vishnu Govindaraj, Juan Manuel Gorriz, Xin Zhang, Yu-Dong Zhang. Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-14.

Chicago/Turabian Style

Shui-Hua Wang; Vishnu Govindaraj; Juan Manuel Gorriz; Xin Zhang; Yu-Dong Zhang. 2021. "Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network." Journal of Ambient Intelligence and Humanized Computing , no. : 1-14.

Research article
Published: 08 March 2021 in Computational and Mathematical Methods in Medicine
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Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

ACS Style

Shui-Hua Wang; Yin Zhang; Xiaochun Cheng; Xin Zhang; Yu-Dong Zhang. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation. Computational and Mathematical Methods in Medicine 2021, 2021, 1 -18.

AMA Style

Shui-Hua Wang, Yin Zhang, Xiaochun Cheng, Xin Zhang, Yu-Dong Zhang. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation. Computational and Mathematical Methods in Medicine. 2021; 2021 ():1-18.

Chicago/Turabian Style

Shui-Hua Wang; Yin Zhang; Xiaochun Cheng; Xin Zhang; Yu-Dong Zhang. 2021. "PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation." Computational and Mathematical Methods in Medicine 2021, no. : 1-18.

Editorial
Published: 05 March 2021 in Computers & Electrical Engineering
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ACS Style

Shuihua Wang; Zhengchao Dong; Shuai Liu. Introduction to the special section on data preprocessing for big biomedical data in deep learning models (VSI-dpbd). Computers & Electrical Engineering 2021, 90, 107054 .

AMA Style

Shuihua Wang, Zhengchao Dong, Shuai Liu. Introduction to the special section on data preprocessing for big biomedical data in deep learning models (VSI-dpbd). Computers & Electrical Engineering. 2021; 90 ():107054.

Chicago/Turabian Style

Shuihua Wang; Zhengchao Dong; Shuai Liu. 2021. "Introduction to the special section on data preprocessing for big biomedical data in deep learning models (VSI-dpbd)." Computers & Electrical Engineering 90, no. : 107054.

Journal article
Published: 13 February 2021 in Biomedicine & Pharmacotherapy
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Metabolic syndrome (MS) is a major global health concern comprising a cluster of co-occurring conditions that increase the risk of heart disease, stroke and type 2 diabetes. MS is usually diagnosed using a combination of physiochemical indexes (such as BMI, abdominal circumference and blood pressure) but largely ignores clinical symptoms when investigating prevention and treatment of the disease. Exploring predictors of MS using multiple diagnostic indicators may improve early diagnosis and treatment of MS. Traditional Chinese medicine (TCM) attaches importance to the etiology of disease symptoms and indications using four diagnostic methods, which have long been used to treat metabolic disease. Therefore, in this study, we aimed to develop predictive indicators for MS using both physiochemical indexes and TCM methods. Clinical information (including both physiochemical and TCM indexes) was obtained from a cohort of 586 individuals across 4 hospitals in China, comprising 136 healthy controls and 450 MS cases. Using this cohort, we compared three classic machine learning methods: decision tree (DT), support vector machine (SVM) and random forest (RF) towards MS diagnosis using physiochemical and TCM indexes, with the best model selected by comparing the accuracy, specificity and sensitivity of the three models. In parallel, the best proportional partition of the training data to the test data was confirmed by observing the changes in evaluation indexes using each model. Next, three subsets containing different categories of variables (including both TCM and physicochemical indexes combined – termed the “fused indexes”, only physicochemical indexes, and TCM indexes only) were compared and analyzed using the best performing model and optimum training to test data proportion. Next, the best subset was selected through comprehensive comparative analysis, and then the important prediction variables were selected according to their weight. When comparing the three models, we found that the RF model had the highest average accuracy (average 0.942, 95%CI [0.925, 0.958]) and sensitivity (average 0.993, 95%CI [0.990, 0.996]). Besides, when the training set accounted for 80% of the cohort data, the specificity got the best value and the accuracy and sensitivity were also very high in RF model. In view of the performance of the three different subsets, the prediction accuracy and sensitivity of models analyzing the fused indexes and only physicochemical indexes remained at a high level. Further, the mean value of specificity of the model using fused indexes was 0.916, which was significantly higher than the model with only physicochemical indexes (average 0.822) and the model with only TCM indexes (average 0.403). Based on the RF model and data allocation ratio (8:2), we further extracted the top 20 most significant variables from the fused indexes, which included 14 physicochemical indexes and 6 TCM indexes including wiry pulse, chest tightness, spontaneous perspiration, greasy tongue coating etc. Compared with SVM and DT models, the RF model showed the best performance, especially when the ratio of the training set to test set is 8:2. Compared with single predictive indexes, the model constructed by combining physiochemical indexes with TCM indexes (i.e. the fused indexes) exhibited better predictive ability. In addition to common physicochemical indexes, some TCM indexes, such as wiry pulse, chest tightness, spontaneous perspiration, greasy tongue coating, can also improve diagnosis of MS.

ACS Style

Shu-Jie Xia; Bi-Zhen Gao; Shui-Hua Wang; David S. Guttery; Can-Dong Li; Yu-Dong Zhang. Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes. Biomedicine & Pharmacotherapy 2021, 137, 111367 .

AMA Style

Shu-Jie Xia, Bi-Zhen Gao, Shui-Hua Wang, David S. Guttery, Can-Dong Li, Yu-Dong Zhang. Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes. Biomedicine & Pharmacotherapy. 2021; 137 ():111367.

Chicago/Turabian Style

Shu-Jie Xia; Bi-Zhen Gao; Shui-Hua Wang; David S. Guttery; Can-Dong Li; Yu-Dong Zhang. 2021. "Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes." Biomedicine & Pharmacotherapy 137, no. : 111367.

Journal article
Published: 05 February 2021 in Multimedia Tools and Applications
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Cerebral microbleed (CMB) is related to cerebral vascular diseases. In this paper, we propose the use of deep convolutional neural network to implement CMB automatic diagnosis based on brain susceptibility-weighted images (SWIs). First of all, a sliding neighborhood method was employed to get 13,031 samples for training and testing. Then, an 18-layer CMB-Net was designed to classify the samples as CMB or non-CMB. The CMB-Net was trained by RMSprop based on the five-fold cross- validation. The total running time of the five-fold cross-validation was merely 184.79 s, and the average testing accuracy reached 98.39%, which was better than several recently published methods. The results suggested that our CMB-Net was accurate in detecting CMB.

ACS Style

Zhihai Lu; Yan Yan; Shui-Hua Wang. CMB-net: a deep convolutional neural network for diagnosis of cerebral microbleeds. Multimedia Tools and Applications 2021, 1 -20.

AMA Style

Zhihai Lu, Yan Yan, Shui-Hua Wang. CMB-net: a deep convolutional neural network for diagnosis of cerebral microbleeds. Multimedia Tools and Applications. 2021; ():1-20.

Chicago/Turabian Style

Zhihai Lu; Yan Yan; Shui-Hua Wang. 2021. "CMB-net: a deep convolutional neural network for diagnosis of cerebral microbleeds." Multimedia Tools and Applications , no. : 1-20.

Article
Published: 18 January 2021 in Cognitive Computation
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COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting “201-IV” can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 ± 1.07, 96.25 ± 1.16, 96.29 ± 1.11, and 96.30 ± 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19.

ACS Style

Yu-Dong Zhang; Suresh Chandra Satapathy; Xin Zhang; Shui-Hua Wang. COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cognitive Computation 2021, 1 -17.

AMA Style

Yu-Dong Zhang, Suresh Chandra Satapathy, Xin Zhang, Shui-Hua Wang. COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cognitive Computation. 2021; ():1-17.

Chicago/Turabian Style

Yu-Dong Zhang; Suresh Chandra Satapathy; Xin Zhang; Shui-Hua Wang. 2021. "COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting." Cognitive Computation , no. : 1-17.

Journal article
Published: 30 December 2020 in Neurocomputing
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The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.

ACS Style

Xiang Yu; Siyuan Lu; Lili Guo; Shui-Hua Wang; Yu-Dong Zhang. ResGNet-C: A graph convolutional neural network for detection of COVID-19. Neurocomputing 2020, 452, 592 -605.

AMA Style

Xiang Yu, Siyuan Lu, Lili Guo, Shui-Hua Wang, Yu-Dong Zhang. ResGNet-C: A graph convolutional neural network for detection of COVID-19. Neurocomputing. 2020; 452 ():592-605.

Chicago/Turabian Style

Xiang Yu; Siyuan Lu; Lili Guo; Shui-Hua Wang; Yu-Dong Zhang. 2020. "ResGNet-C: A graph convolutional neural network for detection of COVID-19." Neurocomputing 452, no. : 592-605.

Journal article
Published: 02 December 2020 in Information Processing & Management
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In a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN). We utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method. As proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%. Our BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.

ACS Style

Yu-Dong Zhang; Suresh Chandra Satapathy; David S. Guttery; Juan Manuel Górriz; Shui-Hua Wang. Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network. Information Processing & Management 2020, 58, 102439 .

AMA Style

Yu-Dong Zhang, Suresh Chandra Satapathy, David S. Guttery, Juan Manuel Górriz, Shui-Hua Wang. Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network. Information Processing & Management. 2020; 58 (2):102439.

Chicago/Turabian Style

Yu-Dong Zhang; Suresh Chandra Satapathy; David S. Guttery; Juan Manuel Górriz; Shui-Hua Wang. 2020. "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network." Information Processing & Management 58, no. 2: 102439.

Journal article
Published: 01 December 2020 in Heliyon
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Brain disease is one of the leading causes of death nowadays. Medical imaging is the most effective method for brain disease diagnosis, which provides a clear view of the interior brain. However, manual interpretation requires too much time and effort because medical images contain a large volume of information. Computer aided diagnosis is playing a more and more significant role in the clinic, which can help doctors and physicians to analyze medical images automatically. In this study, a novel pathological brain detection system was proposed for brain magnetic resonance images based on ResNet and randomized neural networks. Firstly, a ResNet was employed as the feature extractor, which was a famous convolutional neural network structure. Then, we used three randomized neural networks, i.e., the Schmidt neural network, the random vector functional-link net, and the extreme learning machine. The weights and biases in the three networks were trained by the chaotic bat algorithm. The three proposed methods achieved similar results based on five runs, and they yielded comparable performance in comparison with state-of-the-art approaches.

ACS Style

Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. Detecting pathological brain via ResNet and randomized neural networks. Heliyon 2020, 6, e05625 .

AMA Style

Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang. Detecting pathological brain via ResNet and randomized neural networks. Heliyon. 2020; 6 (12):e05625.

Chicago/Turabian Style

Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. 2020. "Detecting pathological brain via ResNet and randomized neural networks." Heliyon 6, no. 12: e05625.

Original article
Published: 22 November 2020 in Complex & Intelligent Systems
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Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.

ACS Style

Yu-Dong Zhang; Suresh Chandra Satapathy; Di Wu; David S. Guttery; Juan Manuel Górriz; Shui-Hua Wang. Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling. Complex & Intelligent Systems 2020, 7, 1295 -1310.

AMA Style

Yu-Dong Zhang, Suresh Chandra Satapathy, Di Wu, David S. Guttery, Juan Manuel Górriz, Shui-Hua Wang. Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling. Complex & Intelligent Systems. 2020; 7 (3):1295-1310.

Chicago/Turabian Style

Yu-Dong Zhang; Suresh Chandra Satapathy; Di Wu; David S. Guttery; Juan Manuel Górriz; Shui-Hua Wang. 2020. "Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling." Complex & Intelligent Systems 7, no. 3: 1295-1310.

Journal article
Published: 13 November 2020 in Information Fusion
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: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.

ACS Style

Shui-Hua Wang; Deepak Ranjan Nayak; David S. Guttery; Xin Zhang; Yu-Dong Zhang. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion 2020, 68, 131 -148.

AMA Style

Shui-Hua Wang, Deepak Ranjan Nayak, David S. Guttery, Xin Zhang, Yu-Dong Zhang. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion. 2020; 68 ():131-148.

Chicago/Turabian Style

Shui-Hua Wang; Deepak Ranjan Nayak; David S. Guttery; Xin Zhang; Yu-Dong Zhang. 2020. "COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis." Information Fusion 68, no. : 131-148.