COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3

Shadin, N. S.; Sanjana, S.; Lisa, N. J.

The COVID-19 has created havoc in our daily life, economy, and health system and spread all over the world. Many diagnostic technologies have been used for early and efficient detection of COVID-19 as it is the way to break off the pandemic. COVID-19 can cause respiratory failure and lung damage. So, chest X-Ray has become one of the reliable diagnostic technologies allied with artificial intelligence (AI) techniques that can be useful to validate doctors' opinions. Our research proposed two models using deep learning-based Convolutional Neural Network (CNN) and transfer learning-based InceptionV3 to detect COVID-19 from Chest X-ray. Multiple datasets containing 1553 Chest X-ray images are used in this research. Our proposed deep learning-based CNN architecture achieved the highest 79.74% training accuracy and the highest 84.92% validation accuracy. On the contrary, transfer learning-based InceptionV3 architecture achieved the highest 85.41% training accuracy and the highest 85.94% validation accuracy. © 2021 IEEE.