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Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.
Sirajuddin Qureshi; Jianqiang Li; Faheem Akhtar; Saima Tunio; Zahid Hussain Khand; Ahsan Wajahat. Analysis of Challenges in Modern Network Forensic Framework. Security and Communication Networks 2021, 2021, 1 -13.
AMA StyleSirajuddin Qureshi, Jianqiang Li, Faheem Akhtar, Saima Tunio, Zahid Hussain Khand, Ahsan Wajahat. Analysis of Challenges in Modern Network Forensic Framework. Security and Communication Networks. 2021; 2021 ():1-13.
Chicago/Turabian StyleSirajuddin Qureshi; Jianqiang Li; Faheem Akhtar; Saima Tunio; Zahid Hussain Khand; Ahsan Wajahat. 2021. "Analysis of Challenges in Modern Network Forensic Framework." Security and Communication Networks 2021, no. : 1-13.
The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network’s weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network’s existence. Consequently, it extends the network’s lifetime, overcomes the data transmission’s energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.
Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing 2021, 1 -30.
AMA StyleTariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Suhail Ashfaq Butt, Allah Ditta, Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing. 2021; ():1-30.
Chicago/Turabian StyleTariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. 2021. "An intelligent fault detection approach based on reinforcement learning system in wireless sensor network." The Journal of Supercomputing , no. : 1-30.
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
Khalil Rehman; Jianqiang Li; Yan Pei; Anaa Yasin; Saqib Ali; Tariq Mahmood. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. Sensors 2021, 21, 4854 .
AMA StyleKhalil Rehman, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali, Tariq Mahmood. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. Sensors. 2021; 21 (14):4854.
Chicago/Turabian StyleKhalil Rehman; Jianqiang Li; Yan Pei; Anaa Yasin; Saqib Ali; Tariq Mahmood. 2021. "Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network." Sensors 21, no. 14: 4854.
Diagnose brain diseases by brain CT images is one of the most common ways. But, it usually takes several (>7) years to train a professional doctor because it is very challenging to diagnose brain diseases correctly. The study on automated assistance of brain CT diagnosis is still limited. In this paper, we research the challenges of this task and propose a method by simulating human doctor diagnosis habits. Our method analyzes a full slice of brain CT images, instead of every single one, to take into account continuous changes of the whole brain structure, simulate the way the doctor diagnoses. To avoid redundancies in a thin slice scan, we propose a redundancy removal and data augmentation method that can both reduce computation complexity and improve performance without information loss. Doctors make a diagnosis by observing several key images and key points in them. Our method achieved this by two steps of attention mechanisms. It can highlight the images and key points that have significant impacts on the prediction and explain the results. We evaluated our method on two public datasets CQ500 and RSNA, which achieved 0.9262 and 0.8650 F1 score respectively. Moreover, an experienced doctor (with 29 years of experience) verified the promising clinical application value of the proposed method through manual experiments.
Guanghui Fu; Jianqiang Li; Ruiqian Wang; Yue Ma; Yueda Chen. Attention-based full slice brain CT image diagnosis with explanations. Neurocomputing 2021, 452, 263 -274.
AMA StyleGuanghui Fu, Jianqiang Li, Ruiqian Wang, Yue Ma, Yueda Chen. Attention-based full slice brain CT image diagnosis with explanations. Neurocomputing. 2021; 452 ():263-274.
Chicago/Turabian StyleGuanghui Fu; Jianqiang Li; Ruiqian Wang; Yue Ma; Yueda Chen. 2021. "Attention-based full slice brain CT image diagnosis with explanations." Neurocomputing 452, no. : 263-274.
With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as R², mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and R² are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station.
Bo Liu; Shuo Yan; Jianqiang Li; Yong Li; Jianlei Lang; Guangzhi Qu. A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing. IEEE Transactions on Computational Social Systems 2021, 8, 578 -588.
AMA StyleBo Liu, Shuo Yan, Jianqiang Li, Yong Li, Jianlei Lang, Guangzhi Qu. A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing. IEEE Transactions on Computational Social Systems. 2021; 8 (3):578-588.
Chicago/Turabian StyleBo Liu; Shuo Yan; Jianqiang Li; Yong Li; Jianlei Lang; Guangzhi Qu. 2021. "A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing." IEEE Transactions on Computational Social Systems 8, no. 3: 578-588.
Recently, deep neural network (DNN) models work incredibly well, and edge computing has achieved great success in real-world scenarios, such as fault diagnosis for large-scale rotational machinery. However, DNN training takes a long time due to its complex calculation, which makes it difficult to optimize and retrain models. To address such an issue, this work proposes a novel fault diagnosis model by combining binarized DNNs (BDNNs) with improved random forests (RFs). First, a BDNN-based feature extraction method with binary weights and activations in a training process is designed to reduce the model runtime without losing the accuracy of feature extraction. Its generated features are used to train an RF-based fault classifier to relieve the information loss caused by binarization. Second, considering the possible classification accuracy reduction resulting from those very similar binarized features of two instances with different classes, we replace a Gini index with ReliefF as the attribute evaluation measure in training RFs to further enhance the separability of fault features extracted by BDNN and accordingly improve the fault identification accuracy. Third, an edge computing-based fault diagnosis mode is proposed to increase diagnostic efficiency, where our diagnosis model is deployed distributedly on a number of edge nodes close to the end rotational machines in distinct locations. Extensive experiments are conducted to validate the proposed method on the data sets from rolling element bearings, and the results demonstrate that, in almost all cases, its diagnostic accuracy is competitive to the state-of-the-art DNNs and even higher due to a form of regularization in some cases. Benefited from the relatively lower computing and storage requirements of BDNNs, it is easy to be deployed on edge nodes to realize real-time fault diagnosis concurrently.
Huifang Li; Guangzheng Hu; Jianqiang Li; Mengchu Zhou. Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests. IEEE Transactions on Automation Science and Engineering 2021, PP, 1 -11.
AMA StyleHuifang Li, Guangzheng Hu, Jianqiang Li, Mengchu Zhou. Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests. IEEE Transactions on Automation Science and Engineering. 2021; PP (99):1-11.
Chicago/Turabian StyleHuifang Li; Guangzheng Hu; Jianqiang Li; Mengchu Zhou. 2021. "Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests." IEEE Transactions on Automation Science and Engineering PP, no. 99: 1-11.
This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.
Qing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access 2020, 8, 223335 -223345.
AMA StyleQing Zhao, Guohong Yao, Faheem Akhtar, Jianqiang Li, Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access. 2020; 8 (99):223335-223345.
Chicago/Turabian StyleQing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. 2020. "An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods." IEEE Access 8, no. 99: 223335-223345.
Jianqiang Li; Lu Liu; Jingchao Sun; Yan Pei; Jijiang Yang; Hui Pan; Shi Chen; Qing Wang. Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods. IEEE Access 2020, 8, 214866 -214881.
AMA StyleJianqiang Li, Lu Liu, Jingchao Sun, Yan Pei, Jijiang Yang, Hui Pan, Shi Chen, Qing Wang. Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods. IEEE Access. 2020; 8 ():214866-214881.
Chicago/Turabian StyleJianqiang Li; Lu Liu; Jingchao Sun; Yan Pei; Jijiang Yang; Hui Pan; Shi Chen; Qing Wang. 2020. "Diagnosis and Knowledge Discovery of Turner Syndrome Based on Facial Images Using Machine Learning Methods." IEEE Access 8, no. : 214866-214881.
Cataract is the most prevailing reason for blindness across the globe, which occupies about 4.2% population of the world. Even with the developments in visual sciences, fundus image-based diagnosis is deemed as a gold standard for cataract detection and grading. Though the increase in the workload of ophthalmologists and complexity of fundus images, the results may be subject to intelligence. Therefore, the development of an automatic method for cataract detection is necessary to prevent visual impairment and save medical resources. This paper aims to provide a novel hybrid convolutional and recurrent neural network (CRNN) for fundus image-based cataract classification. The proposed CRNN fuses the advantages of convolution neural network and recurrent neural network to preserve long- and short-term spatial correlation between the patches. Coupled with transfer learning, we adopt AlexNet, GoogLeNet, ResNet and VGGNet to extract multilevel feature representation and to analyse how well these models perform cataract classification. The results demonstrate that the proposed method outperforms state-of-the-art methods with an average accuracy of 0.9739 for four-class cataract classification and provides a compelling reason to be applied for other retinal diseases.
Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Tariq Mahmood; Li Zhang. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer 2020, 1 -11.
AMA StyleAzhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Tariq Mahmood, Li Zhang. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer. 2020; ():1-11.
Chicago/Turabian StyleAzhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Tariq Mahmood; Li Zhang. 2020. "Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network." The Visual Computer , no. : 1-11.
Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.
Saqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry 2020, 12, 1742 .
AMA StyleSaqib Ali, Jianqiang Li, Yan Pei, Muhammad Saqlain Aslam, Zeeshan Shaukat, Muhammad Azeem. An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification. Symmetry. 2020; 12 (10):1742.
Chicago/Turabian StyleSaqib Ali; Jianqiang Li; Yan Pei; Muhammad Saqlain Aslam; Zeeshan Shaukat; Muhammad Azeem. 2020. "An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification." Symmetry 12, no. 10: 1742.
Colorectal cancer is a type of malignant from the intestinal tract. The accurate diagnosis of colorectal polyps can effectively guarantee the life safety of potential patients. There are supervised radionics methods and deep learning methods when determining whether polyps exist. This paper proposes to obtain global features set from computed tomographic colonography (CTC) images by radionics methods and the local features set using deep convolutional neural network simultaneously. Specifically, we use the chaotic evolution algorithm to optimize the parameters in the support vector machine classifier and random forest classifier. Finally, our hybrid method achieved better classification result by random forest classifier on combinational features in which accuracy is 91.318% from the experiment.
Xiaoyu Zhan; Jianqiang Li; Yan Pei. A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images. Lecture Notes in Electrical Engineering 2020, 1 -9.
AMA StyleXiaoyu Zhan, Jianqiang Li, Yan Pei. A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images. Lecture Notes in Electrical Engineering. 2020; ():1-9.
Chicago/Turabian StyleXiaoyu Zhan; Jianqiang Li; Yan Pei. 2020. "A Computer-Aided Diagnostic System to Detect Polyp in Computed Tomographic Colonography Images." Lecture Notes in Electrical Engineering , no. : 1-9.
Brain tumors are one of the most serious brain diseases, which often result in a short life. However, in developing areas, medical resources are in shortage, which affect the diagnosis of brain tumors. With the development of computer science, many diseases can be diagnosed by telemedicine systems, which help physicians save much time and improve diagnostic accuracy. Therefore, we propose a semantic segmentation method for brain tumors based on nested residual attention networks. It can be deployed in social mx‘edia environment to work as a remote diagnosis system. The proposed method uses an improved residual attention block (RAB) as the basic unit. Based on the improved RAB, a nested RAB is designed to build the proposed method, which has better generalization. The proposed method includes an encoder part, a decoder part and skip connections. The encoder part learns multiple feature representations from inputs and the decoder part utilizes the learnt features to make segmentation predictions. In addition, high-level attention feature maps are exploited to induce low-level feature maps in skip connections to discard useless information. The proposed method is mainly validated on the dataset of Brain Tumor Segmentation challenge (BraTS) 2015. The proposed method achieves an average dice score of 0.87 (0.80, 0.72) for the whole tumor (core tumor, enhancing tumor) regions on BraTS 2015 dataset.
Jingchao Sun; Jianqiang Li; Lu Liu. Semantic segmentation of brain tumor with nested residual attention networks. Multimedia Tools and Applications 2020, 1 -18.
AMA StyleJingchao Sun, Jianqiang Li, Lu Liu. Semantic segmentation of brain tumor with nested residual attention networks. Multimedia Tools and Applications. 2020; ():1-18.
Chicago/Turabian StyleJingchao Sun; Jianqiang Li; Lu Liu. 2020. "Semantic segmentation of brain tumor with nested residual attention networks." Multimedia Tools and Applications , no. : 1-18.
Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false- (positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.
Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access 2020, 8, 165779 -165809.
AMA StyleTariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access. 2020; 8 (99):165779-165809.
Chicago/Turabian StyleTariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. 2020. "A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities." IEEE Access 8, no. 99: 165779-165809.
Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.
Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020, 8, 691 -698.
AMA StyleAzhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Ji-Jiang Yang, Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2020; 8 (6):691-698.
Chicago/Turabian StyleAzhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. 2020. "Automated identification of cataract severity using retinal fundus images." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8, no. 6: 691-698.
In this paper, we propose a diagnosis and classification method of hydrocephalus computed tomography (CT) images using deep learning and image reconstruction methods. The proposed method constructs pathological features differing from the other healthy tissues. This method tries to improve the accuracy of pathological images identification and diagnosis. Identification of pathological features from CT images is an essential subject for the diagnosis and treatment of diseases. However, it is difficult to accurately distinguish pathological features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions, etc. Some study results reported that the ResNet network has a better classification and diagnosis performance than other methods, and it has broad application prospectives in the identification of CT images. We use an improved ResNet network as a classification model with our proposed image reconstruction and information fusion methods. First, we evaluate a classification experiment using the hydrocephalus CT image datasets. Through the comparative experiments, we found that gradient features play an important role in the classification of hydrocephalus CT images. The classification effect of CT images with small information entropy is excellent in the evaluation of hydrocephalus CT images. A reconstructed image containing two channels of gradient features and one channel of LBP features is very effective in classification. Second, we apply our proposed method in classification experiments on CT images of colonography polyps for an evaluation. The experimental results have consistency with the hydrocephalus classification evaluation. It shows that the method is universal and suitable for classification of CT images in these two applications for the diagnosis of diseases. The original features of CT images are not ideal characteristics in classification, and the reconstructed image and information fusion methods have a great effect on CT images classification for pathological diagnosis.
Pengzhi Li; Jianqiang Li; Yueda Chen; Yan Pei; Guanghui Fu; Haihua Xie. Classification and recognition of computed tomography images using image reconstruction and information fusion methods. The Journal of Supercomputing 2020, 77, 2645 -2666.
AMA StylePengzhi Li, Jianqiang Li, Yueda Chen, Yan Pei, Guanghui Fu, Haihua Xie. Classification and recognition of computed tomography images using image reconstruction and information fusion methods. The Journal of Supercomputing. 2020; 77 (3):2645-2666.
Chicago/Turabian StylePengzhi Li; Jianqiang Li; Yueda Chen; Yan Pei; Guanghui Fu; Haihua Xie. 2020. "Classification and recognition of computed tomography images using image reconstruction and information fusion methods." The Journal of Supercomputing 77, no. 3: 2645-2666.
In recent years, a rapid rise in the incidence of Large for gestational age (LGA) neonate is reported, and health care professionals employed themselves to discover the cause. Utmost of the previous studies were cohort or observational studies that employed simple linear or multivariate regression models, and very few of them employed machine learning (ML) schemes. Therefore, this research proposes to use 1 expert-driven and 7 automated feature selection schemes with well-known ML classifiers using 10 and 30 folds cross-validation. The induced results were compared with existing baselines, and Wilcoxon signed-rank test and the Friedman test were also introduced to verify the results. The ranked 20 features of the proposed expert-driven feature selection scheme outperformed amongst 7 automated feature selection schemes with a prediction precision, accuracy, and AUC scores of 0.94606, 0.84529, and 0.86492, respectively. Out of twenty features, eleven features were found similar to twenty ranked features of the automated feature selection schemes subsets. The classification results of the extracted features were utmost identical to the results of twenty features subset proposed by the expert-driven feature selection scheme. Therefore, we suggest pediatricians to refresh LGA diagnosis process with the proposed scheme because of its practical usage and maximum expert involvement.
Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications 2020, 79, 34047 -34077.
AMA StyleFaheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications. 2020; 79 (45-46):34047-34077.
Chicago/Turabian StyleFaheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. 2020. "Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data." Multimedia Tools and Applications 79, no. 45-46: 34047-34077.
Gestational weight is an essential parameter for the pediatrician to clinically evaluate the health of both neonate and the mother. During the last several decades, an increase in the prevalence of Large for Gestational Age (LGA) neonate is reported and several researchers engaged themselves to discover the cause. Most of them conducted observational or retrospective studies that used simple statistical test (i.e. univariate/multivariate logistic regression etc.,). However, machine learning schemes are rarely been employed to discover the cause. In this research, one proposed expert-driven and seven automated feature selection schemes with five well-known machine learning classifiers using (10 & 30)-fold cross-validations are employed for the establishment of an efficient and accurate LGA classification model. Accuracy, precision, and AUC scores are selected for the evaluation of the proposed scheme. Wilcoxon signed rank, friedman, and bonferroni-dunn tests are used to observe the variations among (10 & 30)-fold cross validation results and to rank various feature selection and classification schemes. Two baseline methods are also used to compare the results of the proposed expert-driven feature selection scheme. The top 20 features selected by the proposed expert-driven feature selection scheme outperformed among seven automated feature selection schemes. A comparison analysis is also performed between expert-driven and data-driven feature subsets. Furthermore, with the intersection of proposed expert-driven and data-driven feature subsets, it is foreseen that out of 20 features, 11 features are found similar, which authenticates the proposed scheme. The classification performance of the 11 extracted features is almost similar to the proposed expert-driven feature selection scheme. Ensemble technique is also exploited to build the better and effective LGA classification model.
Guohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering 2020, 105 -112.
AMA StyleGuohong Yao, Jianqiang Li, Yan Pei, Faheem Akhtar, Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering. 2020; ():105-112.
Chicago/Turabian StyleGuohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. 2020. "An Automatic Turner Syndrome Identification System with Facial Images." Lecture Notes in Electrical Engineering , no. : 105-112.
Turner syndrome adheres serious health-related complications with a tendency to affect various organs during different stages of life which includes hypertension, infertility, and retarded growth. The proper diagnosis of TS requires an expensive test named karyotype test which is not easily available in remote health care units in the countryside. Therefore, we proposed to use facial images to detect TS to pursue a higher accuracy of recognition. The proposed scheme achieved the accuracy of 91.3% with mixed feature extraction schemes using thirty principle components selected with criteria that retained 95% of the information from the turner dataset. Moreover, this research is the first that uses facial features to accurately diagnose TS patients and has the capability to help doctors to establish a cost-effective TS prognosis process in remote health care units that lack required health care facilities.
Xiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering 2020, 72 -78.
AMA StyleXiang Gao, Jianqiang Li, Yan Pei, Faheem Akhtar, Qing Wang, Ting Yang, Ke Huang, Jun Li, Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering. 2020; ():72-78.
Chicago/Turabian StyleXiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. 2020. "Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes." Lecture Notes in Electrical Engineering , no. : 72-78.
Cataract is one of the prevailing cause of blindness in the industrial world that accounts for more than 50% of blindness. The early detection of cataract can protect serious threats of visual impairment. Most of the existing work is based on manual extraction of features, but this paper aims at automatic detection of a cataract into its different grades using deep convolutional neural network integrated with data augmentation techniques. The Gaussian-scale space theory and the general data augmentation settings are used to improve the dataset in terms of quality and quantity, which lead to overcome the issues of the unbalanced dataset. The training and testing of the proposed model are performed on both the original dataset and the augmented dataset. The model accuracy of the convolutional neural network with augmented dataset presented in this paper is 0.9691, which shows an optimal performance compared with the original dataset, and other methods.
Azhar Imran; Jianqiang Li; Yan Pei; Fawaz Mokbal; Ji-Jiang Yang; Qing Wang. Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection. Lecture Notes in Electrical Engineering 2020, 148 -160.
AMA StyleAzhar Imran, Jianqiang Li, Yan Pei, Fawaz Mokbal, Ji-Jiang Yang, Qing Wang. Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection. Lecture Notes in Electrical Engineering. 2020; ():148-160.
Chicago/Turabian StyleAzhar Imran; Jianqiang Li; Yan Pei; Fawaz Mokbal; Ji-Jiang Yang; Qing Wang. 2020. "Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection." Lecture Notes in Electrical Engineering , no. : 148-160.
Cancer subtype analysis, as an extension of cancer diagnosis, can be regarded as a consensus clustering problem. This analysis is beneficial for providing patients with more accurate treatment. Consensus clustering refers to a situation in which several different clusters have been obtained for a particular data set, and it is desired to aggregate those clustering results to get a better clustering solution. In this paper, we propose to generalize the traditional consensus clustering methods in three manners: (1) We provide Bregmannian consensus clustering (BCC), where the loss between the consensus clustering result and all the input clusterings are generalized from a traditional Euclidean distance to a general Bregman loss; (2) we generalize the BCC to a weighted case, where each input clustering has different weights, providing a better solution for the final clustering result; and (3) we propose a novel semi-supervised consensus clustering, which adds some must-link and cannot-link constraints compared with the first two methods. Then, we obtain three cancer (breast, lung, colorectal cancer) data sets from The Cancer Genome Atlas (TCGA). Each data set has three data types (mRNA, mircoRNA, methylation), and each is respectively used to test the accuracy of the proposed algorithms for clusterings. The experimental results demonstrate that the highest aggregation accuracy of the weighted BCC (WBCC) on cancer data sets is 90.2%. Moreover, although the lowest accuracy is 62.3%, it is higher than other methods on the same data set. Therefore, we conclude that as compared with the competition, our method is more effective.
Jianqiang Li; Liyang Xie; Yunshen Xie; Fei Wang. Bregmannian consensus clustering for cancer subtypes analysis. Computer Methods and Programs in Biomedicine 2020, 189, 105337 .
AMA StyleJianqiang Li, Liyang Xie, Yunshen Xie, Fei Wang. Bregmannian consensus clustering for cancer subtypes analysis. Computer Methods and Programs in Biomedicine. 2020; 189 ():105337.
Chicago/Turabian StyleJianqiang Li; Liyang Xie; Yunshen Xie; Fei Wang. 2020. "Bregmannian consensus clustering for cancer subtypes analysis." Computer Methods and Programs in Biomedicine 189, no. : 105337.