Chest X-Ray Image Analysis for Respiratory Disease Prediction using Grad-CAM

Abstract

Identification of respiratory disease is a vital step in respiratory disease diagnosis and treatment. Chest X-rays computed tomography (CT), and magnetic resonance imaging (MRI) scans are performed to evaluate the lungs and other constituents of the respiratory system. Chest X-rays are frequently used to assess respiratory diseases like pneumothorax, pneumonia, tuberculosis, and coronavirus disease. By examining the images, doctors can accurately identify the presence of certain conditions, such as abnormal cells, fluid build-up, and lung consolidation. Deep learning techniques can automatically analyze vast amounts of data and spot patterns that human experts might overlook. This may result in recommendations for treatments and diagnoses that are more precise. This proposed work aims to develop a reliable system that can classify chest X-ray images into Coronavirus Disease, Bacterial pneumonia, tuberculosis, and normal cases using convolutional neural networks (CNNs) which will be helpful in the medical field. We used a dataset that consists of 8000 images which belong to various classes. We trained our data on various pre-trained models like VGG-19, Inception Net V3, and ResNet 50 with various learning rates achieved an accuracy of 95%, 87%, and 98% respectively. This proposed work used Grad-CAM to provide insights into which areas the model paid more focus during classifications, which will assist health professionals in starting medication as immediately as possible.