Developing Convolutional Neural Networks-Based System for Predicting Pneumonia Using X-Radiography Image

Abstract

Pneumonia is a respiratory disease caused by Streptococcus Pneumoniae infection. It is a life-threatening disease that causes a high mortality rate for children under 5 years of age every year. Under such circumstances, we have a vital need to develop an appropriate and consistent protocol for the identification and diagnosis of pneumonia. The incorporation of computational approaches into the diagnosis of disease is extremely efficient, promising and reliable. Our goal is to integrate these methods into pneumonia routine diagnosis to save countless lives around the world. We used the machine learning algorithm of Convolutional Neural Networks (CNNs) to identify visual symptoms of pneumonia in X-ray radiographic images and make a diagnostic decision. The dataset used to construct the computational model consists of 5844 X-ray images belonging to the pneumonia affected and normal individuals. Our computational model has been successful in identifying pneumonia patients with a diagnosis accuracy of 84%. Our model may increase the efficiency of the pneumonia diagnosis process and accelerate pathogenicity studies of the disease.