Title: Investigation of COVID-19 symptoms using deep learning based image enhancement scheme for x-ray medical images

Authors: V. Pandimurugan; A.V. Prabu; S. Rajasoundaran; Sidheswar Routray; Nilesh Bhaskarrao Bahadure; D. Ratna Kishore

Addresses: School Department of Computing Science and Engineering, VIT University, Bhopal, Madhya Pradesh, India ' Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India ' School Department of Computing Science and Engineering, VIT University, Bhopal, Madhya Pradesh, India ' Department of Computer Science and Engineering, School of Engineering, Indrashil University, Rajpur, Mehsana, Gujarat, India ' School of Electronics Engineering, Sanjay Ghodawat University, Kolhapur, Maharashtra, India ' Department of IT, Andhra Loyola Institute of Engineering and Technology, Andhra Pradesh, India

Abstract: Image enhancement is the inevitable technique for investigating various biological features. The biological image data can be obtained from computer tomography (CT), magnetic resonance imaging (MRI), and X-ray imaging. X-ray imaging is useful for getting the information from lungs and respiratory system. COVID-19 is a life-threatening contiguous disease for the past two years in the world. Patient's chest images playing an important role in the diagnosis of early detection of disease intensity. We propose a generative adversarial network (GAN) method that identifies COVID-19 from medical images and improves diagnostic sensitivity. Here we used virtual colouring methods and a platform for training the images by using a deep parental training method. Similarly, it gives optimal classification results with the help of well-defined image enhancement techniques and image extraction approaches. In our method, the accuracy level lies between 87.8% and 89.6% correspondingly for the dataset and synthetic dataset.

Keywords: COVID-19; image classification; medical image enhancement generative adversarial network; deep learning.

DOI: 10.1504/IJBM.2023.130636

International Journal of Biometrics, 2023 Vol.15 No.3/4, pp.327 - 343

Received: 17 Jul 2021
Accepted: 06 Oct 2021
Published online: 02 May 2023 *

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