Explainable AI-Enhanced Machine Learning for Reliable Pneumonia Detection in Medical Imaging

Abstract

Pneumonia remains a major cause of lung disease globally, and its timely and reliable diagnosis is crucial. Radiology is often used to detect the infection in a chest X-ray, but this process can be laborious and depend on the radiologist’s interpretation. However, recent advances in deep learning techniques have demonstrated high accuracy in automated pneumonia detection, but their "black-box" nature hampers their practical use. Health-care professionals often need to understand the reasons behind predictions to trust the automated system. This study introduces a machine learning-based approach with additional explainable techniques to enhance model performance and explainability in predicting pneumonia. The proposed method uses a convolutional neural network to predict chest X-ray images, and explainability techniques like Grad-CAM and SHAP are used to explain which parts of the image contribute most to the prediction. We test the system on the RSNA Pneumonia Detection Challenge Dataset that includes expert-provided infection labels. The proposed explanation method is evaluated by comparing the model’s predictions with expert annotations. This study demonstrates that our system not only provides accurate classification results but also provides plausible visual explanations that correspond to the desired locations in the chest. This approach can help boost trust in AI-driven medical diagnostic systems and enable their potential deployment in clinical practice.