Utilizing Deep Convolutional Neural Networks to Identify Pneumonia from Chest X-Ray Images

Abstract

Chest pain, exhaustion, and coughing are all symptoms of pneumonia, a common respiratory infection. Young
children, the elderly, and people with compromised immune systems should avoid it. The diagnostic approach
includes a physical examination, a review of medical history, imaging testing, antibiotics, antiviral medicines, and
supportive treatment. This study suggests using three convolutional neural network (CNN) models to detect
pneumonia: VGG19, DenseNet201, and CheXNet. The goal is to evaluate the performance of many models and
select the most reliable model for pneumonia identification. The VGG19 and DenseNet201 models were trained
and evaluated using a large dataset of chest X-ray images. With a score of 98.22%, our proposed model had the
highest training and tuning accuracy. The upgraded CheXNet model accurately identified a number of patterns
and abnormalities in chest X-ray images associated with pneumonia. These findings highlight the enormous
potential of convolutional neural networks for automated pneumonia diagnosis. More research and validation are
needed to demonstrate its stability and generalizability over a wide range of patient demographics and imaging
techniques.