ArticleDiagnostics (Basel, Switzerland)2021
Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multi-scale information bottleneck with confidence-weighted decision fusion for robust breast ultrasound lesion classification.Frontiers in oncology · 2026Article
- Radiomics analysis of biplanar ultrasound images can discriminate non-mass breast carcinoma from mastitis.Frontiers in oncology · 2026Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.Frontiers in artificial intelligence · 2025Article
- Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions.Scientific reports · 2024Article
- Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.BMC medical imaging · 2024Article
- POST-HOC EXPLAINABILITY OF BI-RADS DESCRIPTORS IN A MULTI-TASK FRAMEWORK FOR BREAST CANCER DETECTION AND SEGMENTATION.IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing · 2023Article
- Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications.Frontiers in oncology · 2023Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
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- A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data.PeerJ. Computer science · 2021Article
- Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.Digital healthArticle
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Authors and funding
8 authors.
Funding
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.
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