Automated Radiograph Analysis for Pneumonia Detection in Healthcare Access

Abstract

Pneumonia affects people across different age categories and is recognized as a serious condition impacting the respiratory system. Early diagnosis of pneumonia is crucial in order to lower the complications and enhance the success rates of cure. Chest X-ray imaging has been commonly employed for the diagnosis of pneumonia. Manual examination of chest X-ray images requires significant time and depends largely on the presence of trained radiology experts. This work proposes system for automatically detecting pneumonia from the chest X-rays using deep learning model. A Convolutional Neural Network (CNN) is trained to classify a chest X ray as Normal and Pneumonia. Methods such as resizing, normalizing, and performing data augmentation on images are used to optimize and increase the accuracy of results. The system also generates a level of confidence for each output. A web application is created to facilitate users in uploading their chest X-rays, and doctors can check and validate results. Experimental results verify a higher accuracy and supremacy of the proposed CNN method compared to a CNN-RNN combined model. The proposed system increases efficiency and helps doctors in diagnosing pneumonia cases.