Predicting Malignancy in Breast Lesions: Enhancing Accuracy with Fine-Tuned Convolutional Neural Network Models

Abstract

Abstract Objectives. This study aimed to explore which convolutional neural network (CNN) model is best for predicting the likelihood of malignancy on dynamic contrast-enhanced breast magnetic resonance imaging (DCE-BMRI). Materials and Methods. A total of 273 benign (benign group) and 274 malignant lesions (malignant group) were obtained, and randomly divided into a training set (benign group: 246 lesions, malignant group: 245 lesions) and a testing set (benign group: 28 lesions, malignant group: 28 lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models (VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2) were evaluated. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, and precision (Pr), recall rate (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. Results. Accuracies of 1.0 were achieved on the training set by all five fine-tuned models (S1-5), with model S4 demonstrating the highest test accuracy at 0.97. Additionally, S4 showed the lowest loss value in the testing set. The S4 model also attained the highest AUC (Area Under the Curve) of 0.89 in the validation set, marking a 13% improvement over the VGG19 model. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. Conclusion. The S4 model we propose emerged as the superior model for predicting the likelihood of malignancy in DCE-BMRI and holds potential for clinical application in patients with breast diseases. However, further validation is necessary, underscoring the need for additional data.