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

arXiv: https://arxiv.org/abs/2401.03173 · HTML: https://mlsanity.com/paper/2401.03173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Vision Transformer for Classification of Breast Ultrasound Images](https://mlsanity.com/paper/2110.14731/) (2021)
- [On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset](https://mlsanity.com/paper/1711.07831/) (2017)
- [Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations](https://mlsanity.com/paper/2203.01933/) (2022)
- [Comparison of different CNNs for breast tumor classification from ultrasound images](https://mlsanity.com/paper/2012.14517/) (2020)
- [Deep learning based prediction of Alzheimer's disease from magnetic resonance images](https://mlsanity.com/paper/2101.04961/) (2021)

## Abstract (arXiv)

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."