Open Access

ARTICLE

Quantum-Enhanced Transparent Contour-Integral Deep Learning for Fair and Explainable Medical Biometric Authentication

Karthick Raghunath K. M.¹, Manjula V.¹, Mahesh T. R.¹, Surbhi B. Khan^2,3,*, Ahmed Alyahya^4,*, Shakila Basheer⁵

1 Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Bangalore, India
2 School of Science, Engineering and Environment, University of Salford, Salford, UK
3 Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
4 Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Authors: Surbhi B. Khan. Email: , ; Ahmed Alyahya. Email:

(This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)

Computer Modeling in Engineering & Sciences 2026, 148(1), 47 https://doi.org/10.32604/cmes.2026.081429

Received 02 March 2026; Accepted 30 April 2026; Issue published 27 July 2026

Abstract

In general, medical biometric datasets, with the essential unique behavioral and physical traits for personalized healthcare, shape the patient identification process, but the tendency towards transparency and fairness is still far away. Most of the existing methods fail to integrate the latest mathematical techniques rigorously with the deep learning models, which eventually makes such models undesirable due to their lack of interpretability and potential bias. As such, in this study, a novel Contour Integrated Transparent Augmented Deep Learning (CITADL) methodology is introduced to bridge this gap. In this study, a structured framework, namely CITADL, combines contour-based mathematical feature transformation with a deep neural network architecture empowered with eXplainable Artificial Intelligence (XAI) modules. By using numerically grounded contour integration techniques within a deep learning module, CITADL detects temporal and spatial patterns in biometric signals based on adaptive fairness regularization and transparency layers, which allow it to explain the decision-making process. The framework is further supplemented through a quantum feature regeneration module that is designed to encode contour-integrated biometric representations in a Variational Quantum Circuit (VQC) and generate enhanced nonlinear correlation modelling. The resulting measured qubits features are decoded and harmoniously injected into the classical deep-learning pipeline, thus retaining the transparency, fairness and explainability. The evaluations of preliminary studies show 97% relative improvement in predictive accuracy and fairness metrics compared to selected baseline models in clinical biometric applications, thus contributing to the development of a reliable and transparent methodology for ethical augmentation of medical biometric data.

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