Classification of diseases on chest X-rays using deep learning

Abstract

Doctors and radiologists are still using manual and visual manners in ordert to diagnose the chest radiographs. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing the chest X-rays. This thesis aims to employ a deep neural network named as stacked auto-encoder for the classification of chest X-rays into normal and abnormal images. The stacked auto-encoder is trained and tested on chest X-rays obtained for public databases which contain normal and abnormal radiographs. A performance based comparison is carried out between two networks where the first one uses input chest X-rays without processing or enhancement and the other one uses input images that are processed and enhanced using histogram equalization. Experimentally, it is concluded that the Stacked auto-encoder achieved a good generalization power in diagnosing the unseen chest X-rays into normal or abnormal. Moreover, it is seen that the enhancement of images using histogram equalization helps in improving the learning and performance of network due to the rise in the accuracy achieved when image are enhanced.