Investigation of COVID-19 symptoms using deep learning based image enhancement scheme for x-ray medical images. (7th February 2023)

• Record Type: Journal Article
• Authors: Pandimurugan, V.; Prabu, A.V.; Rajasoundaran, S.; Routray, Sidheswar; Bahadure, Nilesh Bhaskarrao; Kishore, D. Ratna
• Is Part Of: International journal of biometrics. Volume 15:Number 3/4(2023)
• Page Start: 327
• Page End: 343
• Publication Date: 2023-02-07
• Subjects: COVID-19 -- image classification -- medical image enhancement generative adversarial network -- deep learning; Biometric identification -- Periodicals; Biometry -- Periodicals; 570.15195
• Languages: English
• ISSNs: 1755-8301

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.