Investigation of the performance of Machine Learning Classifiers for Pneumonia Detection in Chest X-ray Images
Rabia Emhamed Al Mamlook, Shengfeng Chen, Hanin Bzizi
Abstract
Pneumonia is one of the serious and life-threatening diseases that is caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period. Therefore, early diagnosis is a significant factor in terms of the successful treatment process. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the Pneumonia detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal(healthy) vs. abnormal(sick). To achieve this, seven existing state-of-the-art Machine Learning techniques and well-known Convolutional Neural Network models have been used to increase efficiency and accuracy. In this study, we propose our Deep Learning for the classification task, which is trained with changed images, through multiple steps of pre-processing. Experimentally, it showed that the Deep Learning technique for the classification task performs the best, compared to the other seven Machine Learning techniques. In this study, we successfully classified chest infection in Chest X-ray Images using Deep Learning based on CNN with an overall accuracy of 98.46%. It achieved a more successful result in detecting Pneumonia cases.