Decoding Pneumonia: Leveraging CNNS for Accurate Chest X-Ray Classification

Abstract

Pneumonia is a known potentially fatal lung disease that is frequently referred to as a silent killer since it can lead to lung alveoli filling with pus or fluid, mainly from fungal, viral, or bacterial infections. Chest X-rays are the primary diagnostic tool for pneumonia; however, the diagnosis becomes more complex when other pulmonary disorders such volume loss, haemorrhage, lung cancer, fluid overload, and consequences from radiation or surgery are taken into account. As a result, the interpretation of chest X-rays becomes complex, which makes the development of computer-aided diagnosis systems necessary to help physicians make decisions that are more accurate. In order to diagnose pneumonia from chest X-ray pictures, the research reported here uses a convolutional neural network (CNN) enhanced with a self-attention mechanism. 'Normal' and 'pneumonia' classes are included in the dataset used in the study methodology, and data augmentation techniques are applied to improve the model's resilience. By means of extensive evaluation metrics and visualizations, the study highlights the potential of the suggested model as a useful instrument to aid clinicians in diagnosing pneumonia, consequently reducing the difficulties linked to the interpretation of chest X-rays in the context of various pulmonary conditions.