Survey on deep learning techniques for thorax disease classification task

Abstract

In recent years, medical image analysis has been widely studied, largely because it provides useful tools to support daily clinical routines. Thorax disease classification (TDC) is an important one among these tools since Chest X-ray (CXR) has become prevalent in daily clinical examinations. It aims to distinguish between normal and abnormal CXR images according to different diseases. To this aim, many thorax disease classification systems have been proposed to increase performance. Most of them adopt deep learning techniques to train a deep neural network (DNN) by using labeled CXR datasets. The TDC performance has been continually refined. Aiming to provide future researchers with the work being done on TDC to date, we review the DNN models for this task. A brief review of each method along with their evaluations on a set of benchmark datasets is included. Moreover, we give a detailed comparison of these methods as well as a conclusion.