Bimodal feature fusion three-branch network for breast cancer diagnosis based on ultrasound image

Qimu Duan; Dinghao Guo

doi:10.1117/12.3031104

19 July 2024 Bimodal feature fusion three-branch network for breast cancer diagnosis based on ultrasound image

Qimu Duan, Dinghao Guo

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812C (2024) https://doi.org/10.1117/12.3031104
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Breast cancer is recognized as one of the most prevalent cancers among women worldwide, and early detection plays a crucial role in improving patient survival rates. Computer-aided diagnostic systems (CAD) can provide additional information to assist doctors in diagnosis. However, there is a scarcity of deep learning models for breast cancer diagnosis based on CEUS videos, and obtaining datasets is challenging. Given the widespread use of CEUS due to its non-invasive and cost-effective nature, there is an urgent need to develop a diagnostic support system specifically for breast cancer CEUS images to aid doctors in diagnosis. In this study, we constructed a dual-mode dataset consisting of contrast-enhanced ultrasound and B-mode ultrasound. We proposed a Bimodal Feature Fusion Three-Branch Network, which achieves crossmodal information fusion through feature fusion and decision fusion collaborative reasoning. We validated our model using a breast cancer video dataset comprising 332 cases. Ultimately, our model achieved an accuracy of 88.24% while maintaining high sensitivity and specificity. These results indicate a reduction in breast cancer misdiagnosis and missed diagnosis rates compared to existing models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qimu Duan and Dinghao Guo "Bimodal feature fusion three-branch network for breast cancer diagnosis based on ultrasound image", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812C (19 July 2024); https://doi.org/10.1117/12.3031104

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KEYWORDS

Feature fusion

Ultrasonography

Breast cancer

Video

Image fusion

Diagnostics

Tumors

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