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Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

Medicine and Health

Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

S. Punitha, T. Stephan, et al.

This groundbreaking research introduces a Computer Aided Diagnosis (CAD) system for COVID-19 detection, leveraging an Artificial Bee Colony (ABC) optimized Artificial Neural Network (ABCNN). The method accurately classifies lung CT images as COVID-19 or non-COVID-19 with an impressive accuracy of 92.37%. Conducted by esteemed authors S Punitha, Thompson Stephan, Ramani Kannan, MUFTI Mahmud, M Shamim Kaiser, and SAMIR Brahim Belhaouari, this study pushes the boundaries of medical image analysis.

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Playback language: English
Abstract
This work proposes a Computer Aided Diagnosis (CAD) system for COVID-19 detection using an Artificial Bee Colony (ABC) optimized Artificial Neural Network (ANN), called ABCNN. The system segments suspicious regions in lung CT images, extracts texture and intensity features, and then uses an optimized ANN (with ABC-optimized input features, initial weights, and hidden nodes) to classify these regions as COVID-19 or non-COVID-19. Evaluated on 470 lung CT images, the ABCNN approach achieved a classification accuracy of 92.37%.
Publisher
IEEE Access
Published On
Jan 13, 2023
Authors
S Punitha, Thompson Stephan, Ramani Kannan, MUFTI Mahmud, M Shamim Kaiser, SAMIR Brahim Belhaouari
Tags
COVID-19
Artificial Neural Network
Computer Aided Diagnosis
Lung CT images
Image segmentation
Machine learning
Artificial Bee Colony

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