MobileNetV2 Based Chest X-Rays Classification
Shammi Kolonne, Chamodi Fernando, Hashara Kumarasinghe, Dulani Meedeniya
Abstract
Diseases in the respiratory system affect many people worldwide and can lead to life-threatening conditions. Pneumonia is an acute infection of the lungs and Coronavirus is a recently emerged respiratory disease that has been recorded in many deaths around the world and announced as a pandemic in early 2020. It is crucial to detect these conditions at an early stage as possible for proper treatment. Among many treatment strategies, chest X-rays are widely used in the diagnostic process. This study presents a deep learning based approach to analyse chest X-ray images to distinguish normal Pneumonia or COVID-19 Pneumonia conditions. We have followed the MobileNetV2 architecture with additional layers added to the top of the architecture. Our results show an average accuracy of 98.65 % and an average recall of 98.15% with 5-fold cross-validation.