Brain Tumor Detection Using Advanced Explainable AI Algorithms

Abstract

Accurate identification of disease stages is essential for delivering effective and timely treatment. This project introduces a machine learning-based system designed to classify the stage of a disease using patient data, including medical images and clinical records. To make the system's decisions more transparent, Explainable AI (XAI) methods, such as Grad-CAM, Canny Edge Detection, and Saliency Maps, are integrated. These techniques provide clear visual feedback, highlighting key features that influenced the model's prediction. The model is trained on a labeled dataset and employs multi-class classification to assess the severity of the condition. The inclusion of XAI not only increases the reliability of the predictions but also improves understanding for healthcare professionals, supporting better clinical decisions.

In addition, the system generates a brief report outlining the diagnosis, predicted stage, and potential recommendations. By combining precision with interpretability, this approach aims to support medical teams and enhance patient care through trustworthy AI-powered diagnostics.