Pneumonia Identification Using Chest X-Ray Images with Deep Learning

Abstract

Detecting pneumonia is a demanding task which always requires looking at chest X-ray images of patients suffering from it. The normal chest X-ray, reveals clear lungs without any areas of abnormal obscurity or opacification in the image. The Bacterial pneumonia, regularly consists of a focal lobar consolidation (a lung tissue which is filled with liquid instead of air) whereas the viral pneumonia shows distinct and more diffuse ‘interstitial’ pattern in both lungs. These characteristics can be identified, only with the help of an experienced radiologist. But these characteristics of pneumonia can also be overlapped with other diseases, further complicating the diagnosis. Our objective is to build a Convolutional Neural Network (CNN, or ConvNet) classifier to detect pneumonia in X-ray images of the patient. The dataset for the images is taken from kaggle—a data science learning and competition platform. Convolutional Neural Network (CNN or ConvNet) is a class of deep neural networks that specialises in analysing images and thus is widely used in computer vision applications such as image classification and clustering, object detection and neural style transfer. The above-stated objective will be implemented using Python as a programming language and using concepts of deep learning and neural networks. The dataset consists of 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are approximately 5000 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).