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An Investigation of COVID-19 Diagnosis and Severity Detection Using Convolutional Neural Networks

  • Conference paper
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Third International Conference on Image Processing and Capsule Networks (ICIPCN 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 514))

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). It is a contagious disease that has infected more than millions of people around the globe. COVID-19 can be diagnosed based on the amount of infection in the lungs. Apart from the gold standard reverse transcription-polymerase chain reaction test, X-rays and CT scans can be used to diagnose and detect COVID-19 severity. Due to technological advancements, deep learning models play a crucial role in COVID-19 analysis because of their efficiency and accuracy. Hence, the present work investigates various research works to detect COVID-19 using a convolutional neural network. It compares architectures such as Inception, MobileNet, DenseNet, and new architectures like CovidNet and CovidSDNet were developed. Some of the investigations in COVID-19 detection were conducted by performing data augmentation and ensembling techniques with and without transfer learning. Most research works used accuracy, precision, recall, and F1-score as the performance metrics for evaluation. The numerical comparison analysis shows that the earlier works achieved an accuracy of about 89 to 98 percent. However, in most investigated research, multiclass classification is performed to classify the given CT scan or X-ray image into COVID-19, normal or pneumonia. In the current situation, there is a possibility that some people with COVID-19 might have other respiratory diseases as well. Hence, the investigations suggest that multi-label classification with convolutional neural networks can be suitable to determine the combination of respiratory problems present along with COVID-19.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    V. Dhanya & Senthilkumar Mathi

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  1. V. Dhanya
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  2. Senthilkumar Mathi
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Correspondence to Senthilkumar Mathi .

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Editors and Affiliations

  1. Department of Electrical Engineering, Da-Yeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Fuqian Shi

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Dhanya, V., Mathi, S. (2022). An Investigation of COVID-19 Diagnosis and Severity Detection Using Convolutional Neural Networks. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_15

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