Investigation of COVID-19 symptoms using deep learning based image enhancement scheme for x-ray medical images
by V. Pandimurugan; A.V. Prabu; S. Rajasoundaran; Sidheswar Routray; Nilesh Bhaskarrao Bahadure; D. Ratna Kishore
International Journal of Biometrics (IJBM), Vol. 15, No. 3/4, 2023

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.

Online publication date: Tue, 02-May-2023

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