Graph-Based Neural Network for Malignancy Detection in Breast Ultrasound Images using Medically Significant Features

Breast ultrasound imaging is widely used for the early detection of malignant breast lesions. Although deep learning models have shown strong performance, most approaches rely solely on raw image features and overlook clinically significant characteristics essential for reliable diagnosis. This study aims to develop a graph-based neural network model that integrates medically meaningful features to improve malignancy detection in breast ultrasound images.

The Breast Ultrasound (BUS) and Breast Ultrasound 2 (BUS2) datasets were pre-processed for region extraction using a bit-wise AND operation. Clinically relevant tumor features, such as shape, texture and statistical attributes, were extracted using Histogram of Oriented Gradients, gray-level co-occurrence matrix (GLCM) and histogram descriptors from the BUS, BUS2 and Mendeley Breast Ultrasound datasets. Each image was represented as a graph node with feature attributes, with the edges constructed using k-nearest neighbors based on feature similarity. A GraphSAGE-based graph neural network classifier with mean, max and long short-term memory (LSTM) aggregators was trained and evaluated.

On the BUS dataset, the proposed approach achieved accuracies of 92.89% (mean), 93.20% (max) and 94.13% (LSTM). On the Mendeley Breast Ultrasound dataset, accuracies reached 99.80% (mean), 99.60% (max) and 99.99% (LSTM). For the BUS2 dataset, accuracies were 71.96% (mean), 73.15% (max) and 73.19% (LSTM). On the combined BUS-BUS2 dataset, the model achieved 86.69% (mean), 86.58% (max) and 87.62% (LSTM).

The proposed GraphSAGE-based graph neural network effectively incorporates clinically significant features for breast ultrasound malignancy detection and demonstrates strong generalization across multiple datasets. By modeling feature-based relationships among ultrasound images through a graph structure, the proposed framework captures similarities between lesions and enhances classification performance. These findings highlight the potential of graph-based representations for improving computer-aided breast cancer diagnosis using medically meaningful descriptors.