A Novel Model for Modeling Cancer Networks in the Early Detection of Malignant Tumors in Breast Cancer Using Complex Convolutional Neural Networks

Arebi, Peyman

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Volume 34, Issue 2 (5-2026)

Journal of Ilam University of Medical Sciences 2026, 34(2): 114-141

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A Novel Model for Modeling Cancer Networks in the Early Detection of Malignant Tumors in Breast Cancer Using Complex Convolutional Neural Networks

Peyman Arebi ^*

Dept of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran , peymanarebi@gmail.com

Abstract: (559 Views)

Introduction: Breast cancer is a leading cause of death among women, requiring advanced diagnostic methods. Traditional techniques like mammography and biopsy have limitations, including low sensitivity in dense tissue and subjectivity. This paper presents a novel data mining method using Graph Convolutional Neural Networks (GCNNs) to capture complex, non-Euclidean relationships in tumour structures, improving diagnostic accuracy.
Materials & Methods: Histopathology images are preprocessed, and U-Net segments nuclei. Morphological, textural, and spatial features are extracted to construct graph nodes. Edges are weighted by spatial distance and feature similarity. Topological metrics (e.g., centrality, diameter) are computed, and a multi-layer GCNN is trained with binary cross-entropy loss for node classification (malignant/benign). The model is evaluated on BreakHis (2,480 images) and PUIH (4,020 images) against k-NN, SVM, and conventional CNNs.
Results: The GCNN method achieved 96.2% accuracy on BreakHis and 94.3% on PUIH, outperforming CNNs (89.4%, 85.7%) and traditional methods (81.1%, 76.4%). F1 scores reached 98.8% and 95.5%, respectively. Training time was 42% faster than CNNs (140 vs. 302 minutes on PUIH). The model also showed greater robustness to noise and data variations.
Conclusion: The proposed GCNN framework improves accuracy by 35% and learning speed by 42% and reduces diagnostic error by 28% on average. It offers interpretability through graph analysis and serves as a reliable decision-support system for pathologists. Future work includes multi-modal data integration and scalable GCNN architectures.

Keywords: Medical Diagnosis, Breast Cancer, Tumor Classification with Complex Network, Convolutional Graph Neural Networks, Feature Exploration

Full-Text [PDF 1493 kb] (171 Downloads)

Editorial Note: Research | Subject: General
Received: 2025/12/6 | Accepted: 2026/02/21 | Published: 2026/05/26

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Arebi P. A Novel Model for Modeling Cancer Networks in the Early Detection of Malignant Tumors in Breast Cancer Using Complex Convolutional Neural Networks. J. Ilam Uni. Med. Sci. 2026; 34 (2) :114-141
URL: http://sjimu.medilam.ac.ir/article-1-8855-en.html

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