Kalafi, Elham Yousef and Jodeiri, Ata and Setarehdan, Seyed Kamaledin and Lin, Ng Wei and Rahmat, Kartini and Taib, Nur Aishah and Ganggayah, Mogana Darshini and Dhillon, Sarinder Kaur (2021) Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics, 11 (10). ISSN 2075-4418, DOI https://doi.org/10.3390/diagnostics11101859.
Full text not available from this repository.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.</p>
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya Research Grant (PRGS) Program Based [Grant No: PRGS 2017-1], FRGS [Grant No: 1/2019 SKK03/UM/01/1] |
| Uncontrolled Keywords: | Breast cancer; Ultrasound; Deep learning; Diagnostic imaging; Classification |
| Subjects: | R Medicine > RC Internal medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 18 Jul 2022 07:03 |
| Last Modified: | 18 Jul 2022 07:03 |
| URI: | http://eprints.um.edu.my/id/eprint/33886 |
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