Pneumonia Detection by Analyzing Xray Images Using MobileNET, ResNET Architecture and Long Short Term Memory

Pneumonia is a common and deadly disease that has been in existence for quite a while. One of the handy tools to detect its presence in the human body is the use of X-rays. After obtaining the X-ray images it is manually analyzed by professionals to find out whether the patient has pneumonia or not. Analyzing X-ray images by different professionals might breed conclusions that are not exactly similar, this is due to the variation of making judgments differs from people to people. Additionally, different people can make different errors while analyzing X-Ray images as it is a manual process. Two different X-ray images produced for the same patient might produce different results based on the rotation of the patient’s body on each of the X-Ray imaging processes. To make things simpler, for both professionals and non-professionals for analyzing images, we have proposed a method using deep learning techniques in the form of ResNet 101, MobileNet, and LSTM to analyze X-Ray images and detect whether the patient has pneumonia or not. A dataset consisting of 5856 X-ray images has been used and the maximum accuracy noted of our proposed LSTM model is 95.2%.