Paper Title
A Quantum-Inspired Deep Learning Framework with RL-Based Pretraining for Breast Cancer Classification
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Registration ID: IJNRD_325533
Published ID: IJNRD2605714
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Keywords
Breast Cancer Classification; Quantum-Inspired Neural Network; Reinforcement Learning; Deep Q-Network (DQN); Residual Network.
Abstract
Breast cancer classification is one of the most imperative areas that may lead to early diagnosis and reduce mortality rates. Classical machine learning methods (Logistic Regression, Random Forests, and SVM) provide useful predictive performance but struggle to fully capture complex nonlinear interactions in biomedical data. This paper proposes a Quantum-Inspired Residual Deep Q-Network (QNet) that combines reinforcement learning pretraining with supervised fine-tuning using the Breast Cancer Wisconsin dataset. The model leverages residual blocks, layer normalization, and activation-based quantum-analog metrics to improve the quality of the representations and stability of learning. Experimental results demonstrate that the proposed QNet achieves higher accuracy, robustness, and ROC-AUC performance compared to the classical models; besides, it allows for deeper interpretability by means of activation metrics, PCA feature visualization, and partial dependence analysis. These findings confirm the role that quantum-inspired neural architectures might play in providing an effective and scalable framework for real-time diagnosis in medicine and clinical decision support.
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How To Cite (APA)
Dr. K. Satya Sandeep, K. Meghana, G. Sailu, K. Madhunika, & P. Sushma Sri (May-2026). A Quantum-Inspired Deep Learning Framework with RL-Based Pretraining for Breast Cancer Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), h182-h196. https://ijnrd.org/papers/IJNRD2605714.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : h182-h196
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Paper Reg. ID: IJNRD_325533
Published Paper Id: IJNRD2605714
Research Area: Other area not in list
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605714.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605714
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