1 research outputs found
Utilizing Deep Convolutional Neural Networks to Identify Pneumonia from Chest X-Ray Images
No full textChest pain, exhaustion, and coughing are all symptoms of pneumonia, a common respiratory infection. Youngchildren, the elderly, and people with compromised immune systems should avoid it. The diagnostic approachincludes a physical examination, a review of medical history, imaging testing, antibiotics, antiviral medicines, andsupportive treatment. This study suggests using three convolutional neural network (CNN) models to detectpneumonia: VGG19, DenseNet201, and CheXNet. The goal is to evaluate the performance of many models andselect the most reliable model for pneumonia identification. The VGG19 and DenseNet201 models were trainedand evaluated using a large dataset of chest X-ray images. With a score of 98.22%, our proposed model had thehighest training and tuning accuracy. The upgraded CheXNet model accurately identified a number of patternsand abnormalities in chest X-ray images associated with pneumonia. These findings highlight the enormouspotential of convolutional neural networks for automated pneumonia diagnosis. More research and validation areneeded to demonstrate its stability and generalizability over a wide range of patient demographics and imagingtechniques. 
