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

HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 June 2025 V1 Latest version Share on HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification Authors : Marwan AIT HADDOU 0009-0008-1734-1721 [email protected] , Marwan Ait , and Mohamed Bennai Authors Info & Affiliations https://doi.org/10.22541/au.175104057.77930599/v1 185 views 103 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study investigates the efficacy of a hybrid quantum-classical model, denoted as HQCM-EBTC, for the automated classification of brain tumors, comparing its performance against a classical counterpart. A comprehensive dataset comprising 7,576 magnetic resonance imaging (MRI) images, encompassing normal brain structures, meningioma, glioma, and pituitary tumors, was employed. The HQCM-EBTC model integrates a quantum processing layer with 5 qubits per circuit, a circuit depth of 2, and 5 parallel circuits, trained via the AdamW optimizer with a composite loss function that combines cross-entropy and attention consistency losses. The results demonstrate that HQCM-EBTC significantly outperforms the classical model, achieving an overall classification accuracy of 96.48% compared to 86.72%. The quantumenhanced model exhibits superior precision, recall, and F1-scores across all tumor classes, particularly in glioma classification. t-SNE visualizations reveal enhanced feature separability within the quantum processing layer, leading to more distinct decision boundaries. Confusion matrix analysis further substantiates a reduction in misclassification rates with HQCM-EBTC. Moreover, attention map analysis, quantified using the Jaccard Index, indicates that HQCM-EBTC produces more localized and accurate tumor region activations, especially at higher confidence thresholds. These findings underscore the potential of quantum-enhanced models to improve brain tumor classification accuracy and localization, offering promising advancements for clinical diagnostic applications. The demonstrated ability of HQCM-EBTC to achieve higher accuracy and more precise tumor localization suggests a significant step forward in applying quantum computing to medical imaging analysis. Supplementary Material File (rep (9).pdf) Download 4.81 MB Information & Authors Information Version history V1 Version 1 27 June 2025 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords brain tumor classification interpretability medical image analysis quantum machine learning Authors Affiliations Marwan AIT HADDOU 0009-0008-1734-1721 [email protected] View all articles by this author Marwan Ait Faculty of Sciences Ben M'sick, Quantum Physics and Spintronic Team, LPMC, Hassan II University of Casablanca View all articles by this author Mohamed Bennai Faculty of Sciences Ben M'sick, Quantum Physics and Spintronic Team, LPMC, Hassan II University of Casablanca View all articles by this author Metrics & Citations Metrics Article Usage 185 views 103 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Marwan AIT HADDOU, Marwan Ait, Mohamed Bennai. HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification. Authorea . 27 June 2025. DOI: https://doi.org/10.22541/au.175104057.77930599/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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