Explainable AI for Brain Tumour Auditing and Tagging

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Brain tumors are a serious health issue; early detection is crucial for successful treatment. Magnetic Resonance Imaging (MRI) is the most accurate non-surgical method for identifying brain tumors, but manual analysis by radiologists is time-consuming and prone to errors. Deep learning models have shown promising results in detecting brain tumors, but their black-box nature and privacy concerns restrict their adoption in real-world scenarios. To address these issues, a new framework called BAT is proposed, which uses state-of-the-art explainable AI models to detect autonomously, segment brain tumors using MRI images, and provide concise explanations for the decision. BAT can be used for clinical auditing or tagging without requiring patient personal information. The key innovation of BAT is its ability to automate tumor detection, segmentation, and stage prediction while ensuring anonymity and interpretability.