Explainable AI: A Combined XAI Framework for Explaining Brain Tumour Detection Models

TL;DR

This paper presents a combined XAI framework integrating multiple interpretability techniques to improve understanding of deep learning models for brain tumour detection, enhancing transparency and trust in medical AI applications.

Contribution

It introduces a novel multi-technique XAI approach that provides layered explanations for brain tumour detection models, surpassing individual methods in interpretability.

Findings

Achieved 91.24% accuracy in tumour detection

Combined XAI methods offer comprehensive insights

Enhanced interpretability of deep learning models in medical imaging

Abstract

This study explores the integration of multiple Explainable AI (XAI) techniques to enhance the interpretability of deep learning models for brain tumour detection. A custom Convolutional Neural Network (CNN) was developed and trained on the BraTS 2021 dataset, achieving 91.24% accuracy in distinguishing between tumour and non-tumour regions. This research combines Gradient-weighted Class Activation Mapping (GRAD-CAM), Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) to provide comprehensive insights into the model's decision-making process. This multi-technique approach successfully identified both full and partial tumours, offering layered explanations ranging from broad regions of interest to pixel-level details. GRAD-CAM highlighted important spatial regions, LRP provided detailed pixel-level relevance and SHAP quantified feature contributions. The integrated approach effectively explained model predictions, including cases with partial tumour visibility thus showing superior explanatory power compared to individual XAI methods. This research enhances transparency and trust in AI-driven medical imaging analysis by offering a more comprehensive perspective on the model's reasoning. The study demonstrates the potential of integrated XAI techniques in improving the reliability and interpretability of AI systems in healthcare, particularly for critical tasks like brain tumour detection.