Detection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning

Abstract

Pneumonia is one of the most fatal diseases caused in the lungs. The diagnosis involves a chest x-ray which is interpreted by a radiologist. Human assisted diagnosis has its own limitations like the availability of an expert, cost, etc and hence an automated method for the detection of pneumonia from x-rays is a necessity. In this research, neural network models were developed to detect pneumonia from the chest x-ray images. Four models namely a basic convolutional neural network (CNN), VGG16, VGG19, InceptionV3 were constructed using CNN and transfer learning methodologies. The models were then trained on a pediatric pneumonia dataset which comprised of 2992 pneumonia and 2972 normal chest xrays. The results were then tested using 854 pneumonia and 849 normal images, and an accuracy of over 97 percent was obtained from all models.