Design of System for Detection of Pneumonia using Deep Learning

Abstract

Pneumonia continues to pose a significant global health challenge, particularly impacting susceptible populations such as the elderly, children, and individuals with weakened immune systems. Conventional diagnostic methodologies, which predominantly depend on the manual analysis of chest X-rays by radiologists, are frequently time-intensive, susceptible to human error, and subject to variability in diagnostic precision. The imperative for more efficient, precise, and interpretable diagnostic frameworks has catalysed the investigation of advanced technologies within the realm of medical imaging. This initiative, entitled "Automatic Detection of Pneumonia Using Deep Learning Techniques," seeks to address these challenges by amalgamating cutting-edge deep learning approaches with attention mechanisms and explainable artificial intelligence. The primary aim is to construct a robust diagnostic system proficient in accurately identifying pneumonia from chest X-ray images while delivering transparent and interpretable outcomes to enhance clinical decision-making. By utilizing convolutional neural networks (CNNs) augmented with attention mechanisms, our methodology concentrates on optimizing feature extraction from medical images, thereby elevating diagnostic accuracy. The integration of explainable AI methodologies, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), guarantees that the predictions of the model remain transparent and comprehensible to healthcare practitioners. Comprehensive validation and experimentation have substantiated the efficacy of our proposed system, achieving elevated accuracy rates and fostering trust in automated diagnostic approaches. This initiative aspires not only to enhance the early detection and management of pneumonia but also to establish a benchmark for the incorporation of advanced artificial intelligence technologies in the healthcare sector, underscoring the significance of transparency and interpretability in AI-driven medical diagnostics.