Title: A deep learning approach for the augmented diagnosis and prediction of infectious lung diseases

Authors: R. Geetha; G. Umarani Srikanth; E. Kamalanaban

Addresses: Department of Computer Science and Engineering, S.A. Engineering College, Chennai 600077, India ' Department of Computer Science and Engineering, Panimalar Engineering College, Chennai 600123, India ' Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India

Abstract: The pandemic coronavirus is an alarming threat to public health nowadays causing severe acute lung and bronchial infection that incurs a high fatality rate in humans. Researchers vigorously work in this area to find solutions for this critical issue by all means. On the other hand, tests to be carried out to determine the survival time of coronavirus infection across different communities of the population is a long-term need. Herein, this research describes a robust deep neural network to diagnose the suspicious patient's chest X-ray (CXR) in detecting the presence of infection rapidly. This simple and rapid scalable approach has the capacity of immediate application in coronavirus diagnosis as well as predicting the spread and infection probability for every individual put under-diagnosis depending upon their health and societal parameters. Our robust deep neural network yields the best result of 97.87% accuracy and is user-friendly compared to existing methods.

Keywords: deep learning; convolutional neural network; CNN; chest X-ray; CXR; rectified linear units; ReLUs; infectious lung disease.

DOI: 10.1504/IJBET.2024.142527

International Journal of Biomedical Engineering and Technology, 2024 Vol.46 No.3, pp.161 - 179

Received: 26 Sep 2023
Accepted: 17 Feb 2024
Published online: 06 Nov 2024 *

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