A Comprehensive Study on Explainable AI Using YOLO and Post Hoc Method on Medical Diagnosis

Abstract Medical imaging plays a pivotal role in disease detection and intervention. The black-box nature of deep learning models, such as YOLOv8, creates challenges in interpreting their decisions. This paper presents a toolset to enhance interpretability in AI based diagnostics by integrating Explainable AI (XAI) techniques with YOLOv8. This paper explores implementation of post hoc methods, including Grad-CAM and Eigen CAM, to assist end users in understanding the decision making of the model. This comprehensive evaluation utilises CT-Datasets, demonstrating the efficacy of YOLOv8 for object detection in different medical fields. This paper compares the interpretability offered by different post hoc methods, shedding light on abnormalities detected by the model. Moreover, this paper introduces a user-friendly interface for end users, incorporating the generated heat maps for intuitive understanding using different CAM algorithms. These findings underscore the importance of XAI in medical image analysis and offer a practical framework for improving interpretability in X-ray diagnostics. The comparison of the different CAM methods can offer a choice for end users to determine the best fit for deployable tools. This work contributes to bridging the gap between sophisticated deep learning models and actionable insights for professionals. Access at https://spritan.github.io/YOLOv8_Explainer/