Identification of Pulmonary Disease with Chest X-ray data using CNN Architecture

Abstract

Pneumonia is an infectious and fatal sickness in breathing that is caused by germs, fungi, or a virus that infects the    human lung sacs with the load full of fluid or pus. The common method used to diagnose pneumonia are using chest x-rays and always needs a medical expert to assess the result of X-ray. This difficult technique of recognizing pneumonia results in a life loss due to improper diagnosis and treatment. This study intends to integrate deep learning methods to reduce the problem. Convolution Neural Network is optimized to perform the complicated task of detecting diseases like pneumonia from a group of chest X-ray images. This is model is based on supervised learning, the output of this system is dependent on the dataset’s quality. VGG16 Architecture which is a deep learning model is finely tuned using transfer learning to achieve higher accuracy. This model extracts attributes from chest X-ray dataset and categorize regardless if the man is affected with pneumonia or not. This model helps to reduce the sickness and describable challenges frequently faced with medical treatment.