Deep Learning based Diagnostic and Severity Assessment Framework for Lung Diseases using Chest Radiographs

Computer-aided diagnosis and prediction of the severity of lung diseases is a promising way to help overburdened medical experts in accelerating and improving their diagnosis. The objective of this work is to investigate the use of deep learning techniques to design a framework for the automatic diagnosis of lung diseases along with the prediction of severity using chest radiographs. We identified input chest radiographs as healthy or belonging to patients with lung disease along with the confidence score of prediction. The unhealthy chest radiograph is further examined to calculate clinical parameters considered in the severity prediction of lung diseases. We calculate clinical parameters such as the extent of lung involvement in disease manifestation, the type of abnormalities present in chest radiographs, and their location in terms of lung zones. We conduct experiments with our in-house Indian database and achieved an accuracy of 95.65% in the classification between healthy and unhealthy chest radiographs. We obtained average precision scores of 0.8128, 1.00, 0.8214, and 0.9650 for the detection of effusion, cavity, lymphadenopathy, and opacity respectively. Experimental results indicated that the proposed framework can be used to provide rapid and cost-effective screening in places where massive traditional testing is not feasible.