Brain Tumour Diagnostics and Analysis (TDA): Segmentation, Classification and Interactive Interface

Brain tumours are a critical global health challenge, accounting for 85–90% of all primary central nervous system tumours, with an estimated 308,102 new cases globally in 2020. These tumours often lead to severe physical and cognitive impacts on patients, but early and accurate diagnosis is essential for improving survival rates through better treatment planning. Current diagnostic methods rely heavily on radiologist expertise, making the process time-consuming, expensive, and resource-intensive, placing strain on healthcare systems. In this work, we aim to address this diagnostic bottleneck by developing an AI-driven solution that integrates various aspects of brain tumour diagnosis (location, type, size, shape, and severity) into one comprehensive platform. Our solution, TDA, builds on the PRCnet model (tumor type and grade classification) and XAI-MRI model (brain tumor segmentation), and offer a comprehensive system capable of predicting all tumour vital characteristics together while providing interactive interface for correction, feedback and validated learning. Experimental evaluations on different scenarios shows that our proposed framework outperforms existing models.