Interpretable Ai For Accurate Lung Pathology Identification Across CT And X-Ray Modalities

Early and accurate diagnosis of lung pathologies is essential to prevent deaths and enhance diagnostic processes in the clinical environment. In this paper, a proposal is made to have an interpretable deep learning framework in the automated detection of lung abnormalities in both the chest CT and X-rays modalities. The system also includes state-of-the-art preprocessing methods, as well as, lightweight convolutional neural networks models to conduct binary (Normal vs. Abnormal) and multi-class classification of diseases, including, COVID-19, Tuberculosis, and Lung Cancer. In order to make clinical transparency, the framework incorporates explainable AI methods, namely Grad-CAM, to create heatmaps indicating areas that have a major impact on the decisions made by the model. It is a web application made of Django, which allows real-time uploading of images, prediction generation, and visualization of interpretability outputs. The system has a high classification accuracy and localization of pathological features as shown in experimental analysis, which proves the system is a worthy decision-support tool in the hands of radiologists. The suggested interpretable AI design is expected to increase the confidence in the diagnoses, facilitate the early detection, and also lead to creating reliable medical imaging solutions.