Classification of Thoracic Pathologies by Using Convolutional Neural Networks

Abstract

The COVID-19 pandemic has made a huge impact on various aspects of life around the globe. An important step in tackling issues caused by COVID-19 and other thoracic pathologies is to find approaches that will automate the detection of such diseases from medical images. Medical images such as chest x-rays are widely available around the globe. Traditional medical image classification techniques required extensive work for finding distinctive features in medical images and using them for building accurate models for classification. The modern approaches are highly focused on using models based on machine learning for similar tasks. Convolutional Neural Networks (CNNs) are a type of artificial neural network based upon the mathematical linear operation called convolution. CNNs have a few different layers and are widely used for computer vision tasks. A major advantage of CNNs is that they could extract spatial features from images automatically. In this work, various convolutional neural networks are implemented and tested for the classification of chest x-ray images from four classes of normal, viral pneumonia, lung opacity, and Covid-19. The most efficient model achieves an accuracy of 90.69% and a recall and precision of 91.25% and 92.03% across all four classes.

Keywords: Medical image classification, Convolutional neural network, X-rays, Covid-19

DOI: 10.54941/ahfe1002792