Thoracic Disease Detection using Deep Learning

ABSTRACT

Thoracic diseases are the most common radiological disorders worldwide especially in India. It is a life-threatening infectious disease affecting breathing organ like thorax and one or both lungs in human body commonly caused by bacteria. Physicians and radiologists are quiet using physical and visual graphical manners in order to diagnose the chest X-rays. Patient's diagnoses are entirely dependent on the consultant given by that chest expert. However, there might be emergency circumstances where radiology experts are too busy or may not be accessible. The timely and early diagnosis of thoracic diseases is very important. To resolve this situation, an algorithm that accept poster anterior (PA) chest X-rays images which classify whether the thorax is infected or not. If a thorax is infected, the proposed model will figure out which type of thoracic disorder is available on that PA view X-ray image. The proposed model can significantly improve the efficiency of doctors by early detection of the diseases using Computer aided diagnosis (CAD) wielding deep learning. Thus, an intelligent and automatic system is required to diagnose the chest radiograph to detect the various thorax related diseases. This research employ a web oriented identification system using deep learning based convolutional neural network algorithms for the detection, classification and early stage diagnosis of chest radiograph into healthy and thoracic disorders patients including COVID-19. The deep learning model is trained and tested on different radiographs which contain normal and numerous thorax disorders patient. Moreover, after developing the neural network model for the early diagnosis of the numerous thoracic disorders, a graphical user interface (web) based disease screening system also described for visualized the accurate diagnosis X-ray images in respective target disease classes. © 2021 IEEE.