TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification

• URL: http://arxiv.org/abs/2605.01999v1
• Date: Sun, 03 May 2026 18:02:36 GMT
• Title: TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
• Abstract summary: We use self-supervised learning (SSL) to study multi-class brain tumor classification.<n>We evaluate four SSL frameworks including SimCLR, BYOL, DINO, and Moco v3 on a dataset of 4,448 MRIs with 17 distinct tumor types.<n>Results show that, when labels are limited, SSL-pretrained models outperform supervised baselines in terms of F1-score, recall, accuracy, and precision.
• Score: 0.9967735666813463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth of annotated datasets restrict the use of supervised deep learning approaches. In this work, we use self-supervised learning (SSL) to study multi-class brain tumor classification. Using a ResNet-50 backbone, we evaluate four SSL frameworks including SimCLR, BYOL, DINO, and Moco v3 on a publicly available dataset of 4,448 MRIs with 17 distinct tumor types. On the dataset, SimCLR achieved 99.64% accuracy, 99.64% precision, 99.64% recall, and 99.64% F1-score. The workflow includes preprocessing, fine-tuning, linear evaluation, and SSL pretraining with data augmentations. Results show that, when labels are limited, SSL-pretrained models outperform supervised baselines in terms of F1-score, recall, accuracy, and precision. Additionally, by providing visual insights into model decisions, Explainable AI techniques (Grad-CAM, Grad-CAM++, EigenCAM) enhance interpretability. These results demonstrate SSL's scalability and dependability in diagnosing brain tumors from unlabeled medical data.

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