UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

Authors

DOI:

https://doi.org/10.4108/eetcasa.4681

Keywords:

Breast Cancer, Classification, Deep Learning, Segmentation, Ultrasonic Image

Abstract

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

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Published

12-01-2024

How to Cite

1.
Minh TC, Quoc NK, Vinh PC, Phu DN, Chi VX, Tan HM. UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis. EAI Endorsed Trans Context Aware Syst App [Internet]. 2024 Jan. 12 [cited 2024 Jun. 17];10. Available from: https://publications.eai.eu/index.php/casa/article/view/4681

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