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

(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-wa...

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Bibliographic Details
Published in:IEEE sensors journal 2022-09, Vol.22 (18), p.17573-17582
Main Authors: Zhang, Yudong, Satapathy, Suresh Chandra, Zhu, Li-Yao, Gorriz, Juan Manuel, Wang, Shuihua
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Artificial neural networks
Computed tomography
Convolutional neural network
Convolutional neural networks
COVID-19
Data augmentation
deep learning
Diagnosis
Gray-scale
Sensors
stochastic pooling
Stochastic processes
Training
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Summary:(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3025855