Identifying Lung Cancer and Chronic Obstructive Pulmonary Diseases using Residual Neural Network

Abstract

In the last ten years, Lung Cancer and Chronic Obstructive Pulmonary Disease (COPD) have become two major diseases in the category of Respiratory Diseases which have lead to a large number of death rates in India and also in other countries. The main reason for the increase in these cases is due to the excessive smoking habit among youngsters and adults. Thus, proper diagnosis of both lung cancer and COPD are important in order to save human life. A fast and effective method to do this is to differentiate accurately among both diseases and provide the required treatment. This paper focuses on efficiently differentiating among chest pathologies in chest X-Ray using different artificial neural networks, machine learning, and deep learning approaches. It shows how an artificial neural network can be used in the prediction of diseases based on the image sets. ResNets help in better feature extraction of the image sets that lead to the correct classification of diseases. The model achieves a better performance in evaluating chest radiograph datasets that depicts the changes caused in a person's lungs when compared to normal lung images such as the formation of small lobes (or) the enlarged arteries in lungs and so on..