Classification of breast lesions on DCE-MRI Data Using Fine-tuned MobileNet

Abstract

Background: Early and rapid diagnosis of breast cancer is very important. Traditional method for detecting and diagnosing breast cancer may lead to a false positive or negative result. Presently, most of the deep learning models used in breast cancer detection prevents their use on mobile phones or low-configuration devices. This study intends to evaluate the capability of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Methods: The diagnostic efficiencies of MobileNetV1_False(V1_False), MobileNetV1_True (V1_True), MobileNetV2_False(V2_False) and MobileNetV2_True(V2_True) were tested and evaluated under the same program architecture and dataset (2124 images in benign group and 2226 images in malignant group). A relatively optimal model was selected (with the highest accuracy in the training set and test set), and two fine-tuning strategies (S0 and S1) were used to modify it. The accuracy (Ac) of different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), f1 score(f1) and area under the receiver operating characteristic curve (AUC), were taken as the performance indicators. Results: The Ac of V1_True (0.9815) was higher than those of V1_False (0.9749), V2_False (0.9672) and V2_True (0.9699). Overfitting were observed in all models. Pr, Rc, f1 and AUC of V1_True were 0.79,0.73,0.74 and 0.74, respectively, they were higher than those of V1_False (0.77,0.73,0.73 and 0.73), V2_False (0.74,0.65,0.66 and 0.65) and V2_True (0.76,0.67,0.68 and 0.67).Conclusion: The MobileNetV1_True model can differentiate between benign and malignant breast lesions on breast DCE-MRI. Future work is necessary to improve the generalization capability of the proposed method.