IRO Journals
Journal of Innovative Image Processing

Quantum-Inspired Explainable Deep Learning Framework for Hepatocellular Carcinoma Detection Using Gene Expression Data

Open Access
Published: 16 July, 2026
Pages: 1046-1075
  • Naveen Sundar G. , Naveen Sundar G.
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Ponmary Pushpa Latha D. , Ponmary Pushpa Latha D.
    Division of Digital Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Digital Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Ilang Jeevan Vishal Raj R. , Ilang Jeevan Vishal Raj R.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Rosario Gilmary , Rosario Gilmary
    Department of Information Technology, IFET College of Engineering, Villupuram, India
    Department of Information Technology, IFET College of Engineering, Villupuram, India
  • Narmadha D. Narmadha D.
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
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How to Cite

G., Naveen Sundar, Ponmary Pushpa Latha D., Ilang Jeevan Vishal Raj R., Rosario Gilmary, and Narmadha D. 2026. “Quantum-Inspired Explainable Deep Learning Framework for Hepatocellular Carcinoma Detection Using Gene Expression Data”. Journal of Innovative Image Processing 8 (3): 1046-75. https://doi.org/10.36548/jiip.2026.3.015.
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Keywords

Biomedical Data Analytics
Cancer Classification
Counterfactual Explanation
Deep Learning
Explainable Artificial Intelligence
Gene Expression Analysis
Hepatocellular Carcinoma
Quantum-Inspired Neural Network

Abstract

The Hepatocellular Carcinoma (HCC) diagnosis using traditional machine learning algorithms based on gene expression data faces high dimensionality, nonlinear interactions between genes, and poor interpretability. This paper proposes a quantum-inspired deep learning model to classify tumor and non-tumor liver samples based on transcriptomic profiles. The proposed model combines trigonometric encoding, parameterized nonlinear transformation, and interaction layers of features in a neural learning framework to improve the representation of gene dependencies. It includes an explainability module based on SHAP attribution and gradient-based counterfactual analysis to facilitate gene-wise explanation of predictions. The cohort-based training and independent evaluation are performed on publicly available HCC gene expression data. Performance is measured in terms of classification, calibration and robustness metrics and compared against traditional deep neural models. The findings suggest better predictive performance and predictable probabilistic actions. The proposed model achieves a high accuracy of 95.6%, a low counterfactual impact score of 0.112, a high stability index of 0.79 and the calibration of ECE: 2.03%, Brier score: 0.091.

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