Pneumonia Detection and Lung Disease Assessment from Chest X-rays: Developing A Diagnostic Support System

Abstract

This research, dedicated to developing an accurate and efficient pneumonia detection system from Chest X-Ray images, highlights the significance of automated tools in enhancing healthcare diagnostics. Its significance lies in the fact that pneumonia is a prevalent respiratory condition that requires timely and accurate diagnosis for effective medical intervention. The project's objective was to make use of convolutional neural networks and image analyses to create an automated diagnostic tool that could assist healthcare professionals in identifying pneumonia with precision and efficiency. To achieve this, the system initially made use of two custom deep learning architectures but ultimately used a pretrained CheXNet-based model, developed by using transfer learning. This choice was made by considering CheXNet’s proven performance in identifying pneumonia and other pulmonary conditions. The project's results proved promising, with the CheXNet-based model achieving high diagnostic accuracy and providing valuable insights into the presence of pneumonia. The system's architecture, using deep learning and the use of DICOM images, demonstrated its effectiveness in improving the accuracy and efficiency of pneumonia diagnosis. Based on the results, this paper further demonstrates a web-based application for interaction with the system. Additionally, it provides information on the work that could be done in the future. Thus, this research contributes to the growing field of medical image analysis and highlights the significance of automated tools in enhancing healthcare diagnostics. The project's outcomes are meant to pave the way for more efficient and accessible methods for pneumonia detection, ultimately benefiting both healthcare providers and patients.