Chatterjee, Priyadarshini, Siddiqui, Shadab, Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310 and Rao, Srikanth (2025) Multi-modal graph neural networks for colposcopy data classification and visualization. Cancers, 17 (9):1521. ISSN 2072-6694 (Online) (doi:10.3390/cancers17091521)

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Abstract

Background: Cervical lesion classification is essential for early detection of cervical cancer. While deep learning methods have shown promise, most rely on single-modal data or require extensive manual annotations. This study proposes a novel Graph Neural Network (GNN)-based framework that integrates colposcopy images, segmentation masks, and graph representations for improved lesion classification.
Methods: We developed a fully connected graph-based architecture using GCNConv layers with global mean pooling and optimized it via grid search. A five-fold cross-validation protocol was employed to evaluate performance before (1–100 epochs) and after fine-tuning (101–151 epochs). Performance metrics included macro-average F1-score and validation accuracy. Visualizations were used for model interpretability.
Results: The model achieved a macro-average F1-score of 89.4% and validation accuracy of 92.1% before fine-tuning, which improved to 94.56% and 98.98%, respectively, after fine-tuning. LIME-based visual explanations validated models focus on discriminative lesion regions.
Conclusions: This study highlights the potential of graph-based multi-modal learning for cervical lesion analysis. Collaborating with the MNJ Institute of Oncology, the framework shows promise for clinical use.

Item Type:	Article
Additional Information:	This article belongs to the Section Methods and Technologies Development.
Uncontrolled Keywords:	cervical lesion classification, graph neural networks (GNNs), hyperparameter optimization, multi-modal data integration
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	19 Nov 2025 14:30
URI:	https://gala.gre.ac.uk/id/eprint/51679

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