Title: Detection of COVID-19 virus using deep learning

Authors: Kewal Mehta; Hritik Patel; Vraj Patel; Ankit K. Sharma

Addresses: Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, 382481, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, 382481, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, 382481, India ' Instrumentation and Control Engineering Department, Institute of Technology, Nirma University, Gujarat, 382481, India

Abstract: Corona Virus Disease of 2019 (COVID-19) is currently the most threatening and major medical challenge in the world. COVID-19 can be detected using X-ray and CT-scan images of the patient's lungs. With the use of deep learning and neural networks, the process of classifying the patient's CT-scan and X-ray images can be expedited. In this paper, we implemented convolutional neural networks (CNN) for detection of COVID-19 in X-ray and CT-scan images of lungs. Several CNN architectures like VGG16, ResNet-50, Inception-v3, DenseNet 201, Xception, and InceptionResnet-v2 have been implemented and comparative analysis is presented. DenseNet 201 CNN architecture is found to be most accurate in detecting COVID-19 for both X-ray and CT-scan images. The quantitative results suggest promising results for the COVID-19 detection task.

Keywords: COVID-19; X-ray; CT-scan; deep learning; neural networks; CNN; convolutional neural network; transfer learning.

DOI: 10.1504/IJCBDD.2021.121619

International Journal of Computational Biology and Drug Design, 2021 Vol.14 No.6, pp.429 - 446

Received: 28 Jul 2021
Accepted: 02 Nov 2021

Published online: 15 Mar 2022 *

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