ArticleJournal of cancer research and clinical oncology2023
Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Graph-Aware and Sequence-Aware Multimodal Deep Learning Framework for Cancer Detection and Risk Analysis from Medical Imaging.Journal of imaging · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Graph neural network-based multi-label classification of breast cancer using multiparametric MRI and clinical features.Discover oncology · 2025Article
- Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.Scientific reports · 2025Article
- A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.Scientific reports · 2025Article
- Identification of Eye Diseases Through Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.PloS one · 2025Article
- Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.Digital healthArticle
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7 authors.
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Abstract
purposeAn automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features.
methodTen informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities.
resultsThe results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph.
conclusionThe proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.
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