Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks
Document Type
Article
Publication Date
10-1-2021
Abstract
The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.
Keywords
Breast cancer, Ultrasound, Deep learning, Diagnostic imaging, Classification
Divisions
fac_med
Funders
University of Malaya Research Grant (PRGS) Program Based [Grant No: PRGS 2017-1],FRGS [Grant No: 1/2019 SKK03/UM/01/1]
Publication Title
Diagnostics
Volume
11
Issue
10
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND