Pneumonia Prediction Using Deep Learning

an important worldwide health problem is pneumonia, which mostly affects vulnerable groups including the elderly and small children. Reducing death rates and improving patient outcomes depend on an accurate and prompt diagnosis of pneumonia. Conventional diagnostic techniques such as radiologists interpreting chest X-rays (CXR), might be subjectively interpreted incorrectly and take a lot of time. In this study, we look into the automatic classification of cases Pneumonia from Chest X-rays (CXR) images, employing deep learning algorithms specifically Convolutional Neural Networks (CNN). A proposed research used a database of 5,216 chest x-ray images, with the number of samples classified as pneumonia being 3,875 and classified as normal 1,341. To serve as a benchmark, a more basic Support Vector Machine model (SVM) was utilized to compare the results of CNN model. The CNN model outperformed the SVM model, which obtained 97.03% accuracy, with a greater accuracy of 97.42% after training. The improved performance of CNN in identifying pneumonia was facilitated by its automated extraction of hierarchical characteristics from pictures. This study offers healthcare practitioners a useful tool by showcasing the ability of deep learning models to increase diagnostic speed and accuracy. The model's handling of multi-class classification, better generalization across a variety of datasets, and system integration into clinical settings for real-time diagnostic help will be the main areas of future effort.