Affiliation:
1. VIT Vellore
2. University of Eastern Finland
Abstract
Though India being home of one out of every six people in the globe, is facing an arduous task of providing healthcare service, especially to the large number of patients in remote areas due to lack of diagnosis support systems and doctors. It is reported that hospitals in rural areas have an insufficient radiologist due to which thousands of cases are usually handled by single doctor. In this context, we aim to develop an AI based computer-aided diagnosis tool, which can classify abnormalities by reading chest X-ray so that it could assist the doctors in arriving at quick diagnosis. We have employed a Convolutional Neural Network (CNN) designed by Google known as XceptionNet to detect those pathologies in ChestX-ray14 data. Further, same data is being used for executing other CNN- ResNet. Finally, both the results obtained are compared to assess the superior CNN model for X-ray level diagnosis.
Publisher
Trans Tech Publications, Ltd.
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