Covid19 Disease Assessment Using CNN Architecture

• Published in: 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) pp. 1 - 6
• Main Authors: C, Mary Shiba, Mishra, Sumit Kumar, Sandhya, S, Vidhya, K., R, Jaichandran, Manjula, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22-02-2023
• Subjects: Accuracy; COVID-19; Deep Learning; Manuals; Neural Network; Noise Removal; Pandemics; Radiology; Reliability theory; Scaling; Training; Website; X-rays
• DOI: 10.1109/ICIPTM57143.2023.10118086

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not.