Advancing Pneumonia Detection Using Deep Learning

Deep learning has made significant advancements and transformative contributions to various aspects of the medical field. Its applications range from medical image analysis to diagnosis and prediction of diseases such as pneumonia. A type of infection known as pneumonia is brought on by bacteria, viruses, or fungus. It results in fluid or pus accumulation in the lungs' air sacs, called alveoli, which results in difficulty to breath. Early detection of pneumonia is crucial for avoiding adverse effects, including death. X-ray imaging is the most frequently used method for detecting pneumonia, although there are other methods as well, such as pulse oximetry and CT scans. The primary goal of this work is to create a precise model that aids in the detection of pneumonia and the category which it belongs to i.e., viral Pneumonia, bacterial Pneumonia. As part of the diagnosis, determine pneumonia using chest X-ray pictures. In the proposed work, deep learning Models like Inception V3 and MobileNetV2 are used to leverage their deep architectures and powerful feature learning capabilities to efficiently On the basis of evaluation criteria like accuracy, precision, and recall on the dataset of chest X-ray images, the suggested model's efficacy is then assessed. The existing work for Pneumonia Detection is Convolution Neural Networks (CNN) which achieved an accuracy of 84.12% this accuracy can be improved by the proposed model.