Efficient and explainable MRI brain tumor classification via Adaptive GAN-based data augmentation

Document Type

Article

Publication Title

Array

Abstract

Accurate brain tumor diagnosis from MRI is critical, but state-of-the-art deep learning (DL) models are computationally expensive and require large datasets, limiting clinical deployment. Classical machine learning (ML) is more efficient but struggles with imbalanced data and a persistent performance gap. This study proposes an XAI-driven Adaptive GAN (AGAN) framework to bridge this gap. The AGAN integrates interpretability scores (e.g., SHAP, Grad-CAM) into its training objective to iteratively refine synthetic sample generation, focusing on improving minority tumor class representation. Evaluated on four diverse public MRI datasets, the AGAN framework demonstrated a 19.7% improvement in minority-class F1-score over the baseline. AGAN-augmented classical ML classifiers, particularly Support Vector Machine (SVM), achieved accuracies up to 0.902, narrowing the performance gap to state-of-the-art DL models (e.g., ResNet50) to within 2.8–5.7%. Critically, the ML pipeline delivered up to 7000× faster training and required up to 240× less memory. These findings validate the XAI-guided, AGAN-augmented ML pipeline as a scalable, resource-efficient, and interpretable alternative for brain tumor diagnosis, highly suitable for deployment in resource-constrained clinical environments.

DOI

10.1016/j.array.2025.100609

Publication Date

12-1-2025

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