CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images

Bhuvan Mittal; JungHwan Oh

doi:10.1117/12.2601065

30 June 2021 CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images

Bhuvan Mittal, JungHwan Oh

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187816 (2021) https://doi.org/10.1117/12.2601065
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus – 2 (SARS-CoV-2). Over 146 million cases and 3.1 million deaths were reported worldwide as of April 27, 2021. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for the early diagnosis of Covid-19. However, this classification involves radiologists’ time and efforts significantly. It is crucial to develop an automated analysis of CT images to save their time and efforts. In this paper, we propose CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose Covid-19 from CT images. We trained and tested the proposed CoviNet using two public datasets with radiologist-labeled CT images. The experimental results show the proposed CoviNet is promising.

Citation Download Citation

Bhuvan Mittal and JungHwan Oh "CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187816 (30 June 2021); https://doi.org/10.1117/12.2601065

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