Title: Dual interactive Wasserstein generative adversarial network optimised with remora optimisation algorithm-based lung disease detection using chest X-ray images

Authors: Beaulah David; P. Mohamed Shameem; K. Ravikumar; G. Simi Margarat

Addresses: Department of Information Technology, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' University of West London, RAK Branch Campus, UAE ' Department of Computer Science and Engineering, RRASE College of Engineering, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Abstract: Numerous prevailing approaches on lung disease identification are exploited with deep learning, but it does not precisely categorise the lung disease and correspondingly it takes high computation time. To engulf these complications, dual interactive Wasserstein generative adversarial network optimised with remora optimisation algorithm-based lung disease detection with chest X-ray images (DIWGAN-ROA-LDD-CXRI) is proposed for classifying COVID-19, normal and pneumonia lung diseases. Initially, the chest X-ray images are gathered via the dataset of chest X-ray (COVID-19 and pneumonia). The extracted features are given to DIWGAN-ROA for effectively categorise the chest X-ray image from COVID-19, normal and pneumonia. The proposed DIWGAN-ROA-LDD-CXRI approach is activated in Python. The performance of the proposed DIWGAN-ROA-LDD-CXRI approach attains 14.54%, 21.56%, 23.15% and 15.45% higher accuracy, 27.33%, 17.71%, 22.22% and 23.37% lower computation time and 21.11%, 28.89%, 29.95% and 28.14% higher AUC value compared with existing methods.

Keywords: chest X-ray images; term frequency-inverse document frequency; dual interactive Wasserstein generative adversarial network; remora optimisation algorithm; ROA.

DOI: 10.1504/IJBIC.2024.137906

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.3, pp.189 - 201

Received: 17 Feb 2023
Accepted: 21 Sep 2023
Published online: 08 Apr 2024 *

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