Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

Abstract

Purpose: An 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.

Method: Ten 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.

Results: The 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.

Conclusion: The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.

Keywords: Clustering analysis; Feature extraction; GNN; Graph; Spearman correlation; Threshold.

Fig. 1

Sample image of benign class and malignant class with their corresponding ground truths

Fig. 2

The proposed framework for the breast cancer classification

Fig. 3

Solidity feature for Benign and Malignant tumors

Fig. 4

Eclipse ratio feature for Benign and Malignant tumors

Fig. 5

Visualization the textures of benign and malignant tumor

Fig. 6

The tumors correlation with the surroundings. a Benign; b Malignant

Fig. 7

Density plots to differentiate the change in feature distribution pattens for benign and malignant tumors

Fig. 8

A visualization of a small portion of the graph

Fig. 9

The optimized FFN block connections used for the GNN model

Fig. 10

Proposed GNN model architecture

Fig. 11

Confusion matrix of the GNN model

Fig. 12

The loss curve and accuracy curve for the proposed GNN model

Fig. 13

K-fold cross validation on the GNN model

Fig. 14

Flow chart of filtering process of the edges

Fig. 15

Performance for graph datasets with different thresholds