Explainability of Brain Tumor Classification Based on Region

Medical image analysis plays a crucial role in modern healthcare, aiding clinicians in diagnosing and treating various medical conditions. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there has been a surge in the development of AI-powered algorithms for medical image analysis. Explainable Artificial Intelligence (XAI) techniques aim to provide interpretable and transparent insights into the decision-making process of AI models, enhancing their usability and trustworthiness in healthcare applications. We review the state-of-the-art XAI methods, including feature visualisation, attention mechanisms, and rule-based systems, and their application to medical image analysis. XAI techniques can pave the way for safer and more effective AI-driven medical solutions, ultimately benefiting healthcare providers and patients. As the healthcare industry continues to embrace AI, integrating XAI into medical image analysis is poised to revolutionize how diseases are detected, diagnosed, and treated. In our work, we are using the deep learning model to classify and the Explainable AI model, which explains the prediction of the model for Brain tumour disease using MRI images. We have used CNN for brain tumour disease classification. Out of 7043 images, we have taken 5722 for training and 1321 for testing and validating the disease. The model achieves 80% of accuracy. Explainable AI models like LIME, SHAP, Integrated Gradients, and Grad-CAM are used to interpret a model's predictions on regions of interest inan image.