Detecting Pneumonia from X-Ray Images of Chest using Deep Convolutional Neural Network

Coughing, chest pain, and exhaustion are symptoms of the common respiratory infection known as pneumonia. It is harmful to young children, the elderly, and anyone with compromised immune systems. The diagnosis process includes physical examination, a review of medical history, imaging testing, and treatment with antibiotics, antiviral drugs, and supportive care. This study proposes using three models—VGG19, DenseNet201, and CheXNet—of convolutional neural networks (CNN) for the identification of pneumonia. The goal is to evaluate the effectiveness of different models and determine which one is most reliable for detecting pneumonia. On a sizable dataset of chest X-ray pictures, the VGG19 and DenseNet201 models were trained and assessed. The best training and tuning accuracy were achieved by our proposed model, which received a score of 98.22%. Numerous patterns and irregularities in chest X-ray images associated with pneumonia were successfully identified using the improved CheXNet model. These results highlight the enormous potential of convolutional neural networks for automating the diagnosis of pneumonia. More research and validation are needed to demonstrate its stability and generalizability across different patient populations and imaging types.