A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling

Zhang, Yudong; Satapathy, Suresh Chandra; Zhu, Li-Yao; Górriz, J. M.; Wang, Shuihua

doi:10.1109/jsen.2020.3025855

Cited by 85 publications

(74 citation statements)

References 24 publications

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“…Wang et al [13] proposed a 3D deep convolutional neural network to detect COVID-19 (DeCovNet). Zhang et al [14] proposed a seven-layer convolutional neural network for COVID-19 diagnosis (7L-CCD). Their performance achieved an accuracy of 94:03 ± 0:80 for the binary classification task (COVID-19 against healthy subjects).…”

Section: Introductionmentioning

confidence: 99%

“…In the future, the explainability of our proposed AI model can be used in patient monitoring [38] and health big data [39]. Some novel network improvement and signal 60 RCBO [7] 6L-CNN [8] CSSNet [11] COVNet [12] DeCovNet [13] 7L-CCD [14] FCONet [15] PSSPNN(Ours) RN-50 [9] RN-18 [10] MAF1 (%) 15 Computational and Mathematical Methods in Medicine processing techniques may help our AI model in future researches, such as filters [40,41], fuzzy [42,43], edge computing [44], knowledge-aid [45,46], autofocus [47], graph integration, and cross-domain knowledge exploitation [48][49][50].…”

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confidence: 99%

“…Comparison to State-of-the-Art Approaches. We compared our proposed PSSPNN method with 9 state-of-theart methods: RCBO [7], 6L-CNN [8], RN-50 [9], RN-18 [10], CSSNet [11], COVNet [12], DeCovNet [13], 7L-CNN-CD [14], and FCONet [15]. All the comparison was carried on the same test set of 10 runs.…”

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confidence: 99%

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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

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.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Nevertheless, available datasets usually require manipulation of 3D-CT volumes and/or segmentation for Region of Interest (RoI) localization. Zhang et al (2020) proposed a new model that starts with 14-way data augmentation techniques applied on the training set. The augmented set was input to a 7-layer CNN network with enhanced stochastic max pooling, which was used to overcome the limitations of traditional max pooling techniques.…”

Section: Related Workmentioning

confidence: 99%

Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

Ibrahim

Youssef

Fathalla

2021

J Ambient Intell Human Comput

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Different respiratory infections cause abnormal symptoms in lung parenchyma that show in chest computed tomography. Since December 2019, the SARS-COV-2 virus, which is the causative agent of COVID-19, has invaded the world causing high numbers of infections and deaths. The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography (CT) imaging. In this paper, a novel computer aided framework (COV-CAF) is proposed for classifying the severity degree of the infection from 3D Chest Volumes. COV-CAF fuses traditional and deep learning approaches. The proposed COV-CAF consists of two phases: the preparatory phase and the feature analysis and classification phase. The preparatory phase handles 3D-CT volumes and presents an effective cut choice strategy for choosing informative CT slices. The feature analysis and classification phase incorporate fuzzy clustering for automatic Region of Interest (RoI) segmentation and feature fusion. In feature fusion, automatic features are extracted from a newly introduced Convolution Neural Network (Norm-VGG16) and are fused with spatial hand-crafted features extracted from segmented RoI. Experiments are conducted on MosMedData: Chest CT Scans with COVID-19 Related Findings with COVID-19 severity classes and SARS-COV-2 CT-Scan benchmark datasets. The proposed COV-CAF achieved remarkable results on both datasets. On MosMedData dataset, it achieved an overall accuracy of 97.76% and average sensitivity of 96.73%, while on SARS-COV-2 CT-Scan dataset it achieves an overall accuracy and sensitivity 97.59% and 98.41% respectively.

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“…Although the above models can achieve high classification accuracy in the binary classification problems, they have unreliable generalization ability when dealing with the multi-classification problems. Zhang (2020) proposed a seven-layer customized CNN to classify COVID-19 images.…”

Section: Introductionmentioning

confidence: 99%

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Zhai

Sun

2021

International Journal of Cognitive Computing in Engineering

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Purpose As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low cost and high accuracy of single plane lesions recognition. The current COVID-19 detection models have shortcomings such as weak robustness, unreliable generalization ability, and long training time. Methods To solve the above problems, our team proposed two novel frameworks and five methods to diagnose COVID-19 based on chest X-ray images. (i) A novel framework – depthwise separable convolutional neural network (DCNN), and we tested Three methods, viz., using LeNet-5, VGG-16, and ResNet-18 as backbones. (ii) A novel framework – dilated and depthwise separable convolutional neural network (DDCNN), and we tested Two methods, viz., using VGG-16 and ResNet-18 as backbones. Results Experiment results show that our models not only improve the detection accuracy, but also reduce the training time. Conclusions Our methods are superior to state-of-the-art methods in both above aspects.

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A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling

Cited by 85 publications

References 24 publications

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

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