EXAIOC: Explainable AI for Oral Cancer Diagnosis and Prognosis - An Application-Centric Approach for Early Detection and Treatment Planning

We introduce the EXAIOC approach, an Explainable AI method for Oral Cancer Detection and Prognosis utilizing a Convolutional Neural Network (CNN) trained on a comprehensive dataset of nearly 1000 oral cancer images. This system is designed to enhance the interpretability and reliability of AI in medical diagnostics. It incorporates fuzzy logic to handle data uncertainties, improving decision-making processes. We evaluate the model's effectiveness using key metrics: accuracy, AUC-ROC, sensitivity, and specificity. Techniques like Layer-wise Relevance Propagation (LRP) or Grad-CAM are integrated to provide visual explanations of the AI's reasoning, fostering trust among healthcare professionals and aiding in precise clinical decision-making. Words.