Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

Punitha, S. and Stephan, T. and Kannan, R. and Mahmud, M. and Kaiser, M.S. and Belhaouari, S.B. (2023) Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach. IEEE Access. p. 1. ISSN 21693536

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37 when evaluated using a set of 470 lung CT images. Author

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Biological organs; Computer aided diagnosis; Computerized tomography; Extraction; Feature extraction; Neural networks; Optimization, Artificial bee colony algorithm; Artificial bees; Bee colony algorithms; Classification accuracy; Computed tomography; Features extraction; Lung; Multilayers perceptrons; Resilient backpropagation; Solid modelling; Texture features, COVID-19
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 17 Feb 2023 12:58
Last Modified: 17 Feb 2023 12:58
URI: http://scholars.utp.edu.my/id/eprint/34329

Actions (login required)

View Item
View Item