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An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science, Beijing Technology and Business University, Beijing 100048, China
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Academic Editors: Chanda Pritam, William B. Sherwin and Philip Broadbridge
Entropy 2021, 23(2), 204; https://doi.org/10.3390/e23020204
Received: 13 January 2021 / Revised: 27 January 2021 / Accepted: 4 February 2021 / Published: 7 February 2021
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field. View Full-Text
Keywords: visual interpretation; deep learning; machine learning; COVID-19; computer-aided diagnosis; CT images visual interpretation; deep learning; machine learning; COVID-19; computer-aided diagnosis; CT images
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MDPI and ACS Style

Wan, Y.; Zhou, H.; Zhang, X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. Entropy 2021, 23, 204. https://doi.org/10.3390/e23020204

AMA Style

Wan Y, Zhou H, Zhang X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. Entropy. 2021; 23(2):204. https://doi.org/10.3390/e23020204

Chicago/Turabian Style

Wan, Yuchai, Hongen Zhou, and Xun Zhang. 2021. "An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis" Entropy 23, no. 2: 204. https://doi.org/10.3390/e23020204

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