HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification

• URL: http://arxiv.org/abs/2506.21937v1
• Date: Fri, 27 Jun 2025 06:16:57 GMT
• Title: HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification
• Authors: Marwan Ait Haddou, Mohamed Bennai,
• Abstract summary: HQCM-EBTC is a hybrid quantum-classical model for automated brain tumor classification using MRI images.<n>We trained on a dataset of 7,576 scans covering normal, meningioma, glioma, and pituitary classes.<n> HQCM-EBTC achieves 96.48% accuracy, substantially outperforming the classical baseline (86.72%)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose HQCM-EBTC, a hybrid quantum-classical model for automated brain tumor classification using MRI images. Trained on a dataset of 7,576 scans covering normal, meningioma, glioma, and pituitary classes, HQCM-EBTC integrates a 5-qubit, depth-2 quantum layer with 5 parallel circuits, optimized via AdamW and a composite loss blending cross-entropy and attention consistency. HQCM-EBTC achieves 96.48% accuracy, substantially outperforming the classical baseline (86.72%). It delivers higher precision and F1-scores, especially for glioma detection. t-SNE projections reveal enhanced feature separability in quantum space, and confusion matrices show lower misclassification. Attention map analysis (Jaccard Index) confirms more accurate and focused tumor localization at high-confidence thresholds. These results highlight the promise of quantum-enhanced models in medical imaging, advancing both diagnostic accuracy and interpretability for clinical brain tumor assessment.

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