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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.
Wang, Shui-Hua; Zhang, Yin; Cheng, Xiaochun; Zhang, Xin; Zhang, Yu-Dong.
  • Wang SH; School of Computer Science, Henan Polytechnic University, China, Henan 454001, China.
  • Zhang Y; School of Architecture Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK.
  • Cheng X; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Zhang X; School of Science & Technology, Middlesex University, London NW4 4BT, UK.
  • Zhang YD; Department of Medical Imaging, The Fourth People's Hospital of Huai'an, Huai'an, Jiangsu Province 223002, China.
Comput Math Methods Med ; 2021: 6633755, 2021.
Article in English | MEDLINE | ID: covidwho-1140372
ABSTRACT

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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / Tuberculosis, Pulmonary / Diagnosis, Computer-Assisted / Neural Networks, Computer / Community-Acquired Infections / Imaging, Three-Dimensional / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans Language: English Journal: Comput Math Methods Med Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 2021

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / Tuberculosis, Pulmonary / Diagnosis, Computer-Assisted / Neural Networks, Computer / Community-Acquired Infections / Imaging, Three-Dimensional / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans Language: English Journal: Comput Math Methods Med Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 2021