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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.
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 StyleXiang 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 StyleXiang 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.
To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) of the same scene. Deep learning has been widely applied in the field of HSI-MSI fusion, which is limited with hardware. In order to break the limits, we construct an unsupervised multiattention-guided network named UMAG-Net without training data to better accomplish HSI-MSI fusion. UMAG-Net first extracts deep multiscale features of MSI by using a multiattention encoding network. Then, a loss function containing a pair of HSI and MSI is used to iteratively update parameters of UMAG-Net and learn prior knowledge of the fused image. Finally, a multiscale feature-guided network is constructed to generate an HR-HSI. The experimental results show the visual and quantitative superiority of the proposed method compared to other methods.
Shuaiqi Liu; Siyu Miao; Jian Su; Bing Li; Weiming Hu; Yu-Dong Zhang. UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 7373 -7385.
AMA StyleShuaiqi Liu, Siyu Miao, Jian Su, Bing Li, Weiming Hu, Yu-Dong Zhang. UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 ():7373-7385.
Chicago/Turabian StyleShuaiqi Liu; Siyu Miao; Jian Su; Bing Li; Weiming Hu; Yu-Dong Zhang. 2021. "UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 7373-7385.
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.
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 StyleShuihua 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 StyleShuihua 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.
Aim. This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods. Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results. The results on ten runs of 10-fold crossvalidation show that our SOSPCNN model yields a sensitivity of 92.25 ± 2.19 , a specificity of 92.75 ± 2.49 , a precision of 92.79 ± 2.29 , an accuracy of 92.50 ± 1.18 , an F1 score of 92.48 ± 1.17 , an MCC of 85.06 ± 2.38 , an FMI of 92.50 ± 1.17 , and an AUC of 0.9587. Conclusion. The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.
Shui-Hua Wang; Kaihong Wu; Tianshu Chu; Steven L. Fernandes; Qinghua Zhou; Yu-Dong Zhang; Jian Sun. SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition. Wireless Communications and Mobile Computing 2021, 2021, 1 -17.
AMA StyleShui-Hua Wang, Kaihong Wu, Tianshu Chu, Steven L. Fernandes, Qinghua Zhou, Yu-Dong Zhang, Jian Sun. SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition. Wireless Communications and Mobile Computing. 2021; 2021 ():1-17.
Chicago/Turabian StyleShui-Hua Wang; Kaihong Wu; Tianshu Chu; Steven L. Fernandes; Qinghua Zhou; Yu-Dong Zhang; Jian Sun. 2021. "SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition." Wireless Communications and Mobile Computing 2021, no. : 1-17.
Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus (2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine–cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.
Sidharth Purohit; Suresh Chandra Satapathy; S Sibi Chakkaravarthy; Yu-Dong Zhang. Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes. Arabian Journal for Science and Engineering 2021, 1 -13.
AMA StyleSidharth Purohit, Suresh Chandra Satapathy, S Sibi Chakkaravarthy, Yu-Dong Zhang. Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes. Arabian Journal for Science and Engineering. 2021; ():1-13.
Chicago/Turabian StyleSidharth Purohit; Suresh Chandra Satapathy; S Sibi Chakkaravarthy; Yu-Dong Zhang. 2021. "Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes." Arabian Journal for Science and Engineering , no. : 1-13.
Target enhancement is the most important task in a video surveillance system. In order to improve the accuracy and efficiency of target enhancement, and better deal with the subsequent recognition, tracking, behaviour understanding and other processing of targets, a deep learning-based image enhancement algorithm for video surveillance scenes is proposed. First, the super-resolution reconstruction of the image is carried out through the image super-resolution reconstruction method based on the hybrid deep convolutional network to improve the sharpness of the image. Then, for the reconstructed video surveillance scene image, the watershed image enhancement algorithm based on morphology and region merging is used to realize the enhancement of the video surveillance scene image. Deep learning algorithms can improve the accuracy of image enhancement through iterative calculations. Experimental results show that after image enhancement in daytime, night and noisy video surveillance scenes, the maximum enhancement difference rate is less than 0.5%, the cross-linking degree is close to 1, and the average image enhancement time is less than 1.3 s. It can realize image enhancement of video surveillance scenes and improve the image clarity of the video surveillance scene.
Wei‐Wei Shen; Lin Chen; Shuai Liu; Yu‐Dong Zhang. An image enhancement algorithm of video surveillance scene based on deep learning. IET Image Processing 2021, 1 .
AMA StyleWei‐Wei Shen, Lin Chen, Shuai Liu, Yu‐Dong Zhang. An image enhancement algorithm of video surveillance scene based on deep learning. IET Image Processing. 2021; ():1.
Chicago/Turabian StyleWei‐Wei Shen; Lin Chen; Shuai Liu; Yu‐Dong Zhang. 2021. "An image enhancement algorithm of video surveillance scene based on deep learning." IET Image Processing , no. : 1.
Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately. Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance. Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852. Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.
Shui-Hua Wang; Qinghua Zhou; Ming Yang; Yu-Dong Zhang. ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation. Frontiers in Aging Neuroscience 2021, 13, 1 .
AMA StyleShui-Hua Wang, Qinghua Zhou, Ming Yang, Yu-Dong Zhang. ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation. Frontiers in Aging Neuroscience. 2021; 13 ():1.
Chicago/Turabian StyleShui-Hua Wang; Qinghua Zhou; Ming Yang; Yu-Dong Zhang. 2021. "ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation." Frontiers in Aging Neuroscience 13, no. : 1.
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.
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 StyleSi-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 StyleSi-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.
The accelerating power of deep learning in diagnosing a disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical information in recent years. These pose challenges, demands, and opportunities for new AI methods and computational models for efficient data processing, analysis, and modeling with the generated data that are important for clinical applications and in understanding the underlying biological process.
Yu-Dong Zhang; Zhengchao Dong; Juan Manuel Gorriz; Yizhang Jiang; Ming Yang; Shui-Hua Wang. IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering. IEEE Access 2021, 9, 74038 -74043.
AMA StyleYu-Dong Zhang, Zhengchao Dong, Juan Manuel Gorriz, Yizhang Jiang, Ming Yang, Shui-Hua Wang. IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering. IEEE Access. 2021; 9 ():74038-74043.
Chicago/Turabian StyleYu-Dong Zhang; Zhengchao Dong; Juan Manuel Gorriz; Yizhang Jiang; Ming Yang; Shui-Hua Wang. 2021. "IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering." IEEE Access 9, no. : 74038-74043.
Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous time period. In this model, the spatio-temporal features are fused with the weights of dual attention learning, and the fused features are input into softmax classifier for emotion classification. In addition, we utilize SJTU Emotion EEG Dataset (SEED) to appraise the feasibility and effectiveness of the proposed algorithm. Finally, experimental results display that the 3DCANN method has superior performance over the state-of-the-art models in EEG emotion recognition.
Shuaiqi Liu; Xu Wang; Ling Zhao; Bing Li; Weiming Hu; Jie Yu; Yudong Zhang. 3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition. IEEE Journal of Biomedical and Health Informatics 2021, PP, 1 -1.
AMA StyleShuaiqi Liu, Xu Wang, Ling Zhao, Bing Li, Weiming Hu, Jie Yu, Yudong Zhang. 3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition. IEEE Journal of Biomedical and Health Informatics. 2021; PP (99):1-1.
Chicago/Turabian StyleShuaiqi Liu; Xu Wang; Ling Zhao; Bing Li; Weiming Hu; Jie Yu; Yudong Zhang. 2021. "3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition." IEEE Journal of Biomedical and Health Informatics PP, no. 99: 1-1.
Specify the significance of multimodality data fusion in neuroimaging. Summarize the challenges of multimodality data fusion in neuroimaging. Provide recent research advances in multimodality data fusion in neuroimaging.
Yu-Dong Zhang; Juan Manuel Gorriz; Zhengchao Dong. Advances in multimodality data fusion in neuroimaging. Information Fusion 2021, 76, 87 -88.
AMA StyleYu-Dong Zhang, Juan Manuel Gorriz, Zhengchao Dong. Advances in multimodality data fusion in neuroimaging. Information Fusion. 2021; 76 ():87-88.
Chicago/Turabian StyleYu-Dong Zhang; Juan Manuel Gorriz; Zhengchao Dong. 2021. "Advances in multimodality data fusion in neuroimaging." Information Fusion 76, no. : 87-88.
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.
Jian Wang; Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. A review on extreme learning machine. Multimedia Tools and Applications 2021, 1 -50.
AMA StyleJian Wang, Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang. A review on extreme learning machine. Multimedia Tools and Applications. 2021; ():1-50.
Chicago/Turabian StyleJian Wang; Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. 2021. "A review on extreme learning machine." Multimedia Tools and Applications , no. : 1-50.
Over recent years, deep learning (DL) has established itself as a powerful tool across a broad spectrum of domains in imaging—e
Yudong Zhang; Juan Gorriz; Zhengchao Dong. Deep Learning in Medical Image Analysis. Journal of Imaging 2021, 7, 74 .
AMA StyleYudong Zhang, Juan Gorriz, Zhengchao Dong. Deep Learning in Medical Image Analysis. Journal of Imaging. 2021; 7 (4):74.
Chicago/Turabian StyleYudong Zhang; Juan Gorriz; Zhengchao Dong. 2021. "Deep Learning in Medical Image Analysis." Journal of Imaging 7, no. 4: 74.
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.
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 StyleYu‐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 StyleYu‐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.
The present research work is to puts forth the numerical solutions of the nonlinear second-order Lane-Emden-pantograph (LEP) delay differential equation by using the approximation competency of the artificial neural networks (ANNs) trained with the combined strengths of global/local search exploitation of genetic algorithm (GA) and active-set (AS) method, i.e., ANNGAAS. In the proposed ANNGAAS, the objective function is designed by using the mean square error function with continuous mappings of ANNs for the LEP delay differential equation. The training of these constructed networks is conducted proficiently using the integrated capability of global search with GA and assisted local search along with AS approach. The performance of design computing paradigm ANNGAAS is evaluated effectively on variants of LEP delay differential models, while the statistical investigations based on different operators further validate the accuracy and convergence.
Zulqurnain Sabir; Muhammad Asif Zahoor Raja; Hafiz Abdul Wahab; Gilder Cieza Altamirano; Yu-Dong Zhang; Dac-Nhuong Le. Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models. Mathematics and Computers in Simulation 2021, 188, 87 -101.
AMA StyleZulqurnain Sabir, Muhammad Asif Zahoor Raja, Hafiz Abdul Wahab, Gilder Cieza Altamirano, Yu-Dong Zhang, Dac-Nhuong Le. Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models. Mathematics and Computers in Simulation. 2021; 188 ():87-101.
Chicago/Turabian StyleZulqurnain Sabir; Muhammad Asif Zahoor Raja; Hafiz Abdul Wahab; Gilder Cieza Altamirano; Yu-Dong Zhang; Dac-Nhuong Le. 2021. "Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models." Mathematics and Computers in Simulation 188, no. : 87-101.
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.
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 StyleShui-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 StyleShui-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.
With the development of 5G communication and transportation infrastructure, transportation systems face challenges to serve future smart cities regarding effective operation and cost optimization for electric vehicle networks. Thus, heterogeneous networking optimization approaches for these vehicles have been investigated, which have great potential in real-time communications, intelligent processing, reliable understanding, and efficient management. The guest editors have selected 16 articles for review in this special issue. A summary of these articles is outlined below.
Honghao Gao; Yudong Zhang. Guest Editorial Optimization of Electric Vehicle Networks and Heterogeneous Networking in Future Smart Cities. IEEE Transactions on Intelligent Transportation Systems 2021, 22, 1748 -1751.
AMA StyleHonghao Gao, Yudong Zhang. Guest Editorial Optimization of Electric Vehicle Networks and Heterogeneous Networking in Future Smart Cities. IEEE Transactions on Intelligent Transportation Systems. 2021; 22 (3):1748-1751.
Chicago/Turabian StyleHonghao Gao; Yudong Zhang. 2021. "Guest Editorial Optimization of Electric Vehicle Networks and Heterogeneous Networking in Future Smart Cities." IEEE Transactions on Intelligent Transportation Systems 22, no. 3: 1748-1751.
(Aim) To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network. Second, convolutional block attention module (CBAM) was introduced as attention module into our VSBN. Third, an improved multiple-way data augmentation method was used to resist overfitting of our AI model. In all, our model was dubbed as a 12-layer attention-based VGG-style network for COVID-19 (AVNC) (Results) This proposed AVNC achieved the sensitivity/precision/F1 per class all above 95%. Particularly, AVNC yielded a micro-averaged F1 score of 96.87%, which is higher than 11 state-of-the-art approaches. (Conclusion) This proposed AVNC is effective in recognizing COVID-19 diseases.
Shui-Hua Wang; Steven Fernandes; Ziquan Zhu; Yu-Dong Zhang. AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM. IEEE Sensors Journal 2021, PP, 1 -1.
AMA StyleShui-Hua Wang, Steven Fernandes, Ziquan Zhu, Yu-Dong Zhang. AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM. IEEE Sensors Journal. 2021; PP (99):1-1.
Chicago/Turabian StyleShui-Hua Wang; Steven Fernandes; Ziquan Zhu; Yu-Dong Zhang. 2021. "AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM." IEEE Sensors Journal PP, no. 99: 1-1.
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.
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 StyleShu-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 StyleShu-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.
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
Xiangbin Liu; Liping Song; Shuai Liu; Yudong Zhang. A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability 2021, 13, 1224 .
AMA StyleXiangbin Liu, Liping Song, Shuai Liu, Yudong Zhang. A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability. 2021; 13 (3):1224.
Chicago/Turabian StyleXiangbin Liu; Liping Song; Shuai Liu; Yudong Zhang. 2021. "A Review of Deep-Learning-Based Medical Image Segmentation Methods." Sustainability 13, no. 3: 1224.