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."

UGGNet 是一种结合了 U-Net 和 VGG 架构的新型模型，用于增强乳腺超声图像分析的性能，提供准确的乳腺超声图像诊断的综合解决方案。

UGGNet: U-Net 和 VGG 的高级乳腺癌诊断桥梁

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

In this work, we investigate the inference time of the MobileNet family,
EfficientNet V1 and V2 family, VGG models, Resnet family, and InceptionV3 on
four edge platforms. Specifically NVIDIA Jetson Nano, Intel Neural Stick,
Google Coral USB Dongle, and Google Coral PCIe. Our main contribution is a
thorough analysis of the aforementioned models in multiple settings, especially
as a function of input size, the presence of the classification head, its size,
and the scale of the model. Since throughout the industry, those architectures
are mainly utilized as feature extractors we put our main focus on analyzing
them as such. We show that Google platforms offer the fastest average inference
time, especially for newer models like MobileNet or EfficientNet family, while
Intel Neural Stick is the most universal accelerator allowing to run most
architectures. These results should provide guidance for engineers in the early
stages of AI edge systems development. All of them are accessible at
this https URL

本研究分析了 MobileNet，EfficientNet，VGG，Resnet 和 InceptionV3 等多个卷积神经网络在多种设置下的推理时间，结果发现 Google 平台的推理速度最快，特别是对于 MobileNet 或 EfficientNet 等较新的模型；而 Intel Neural Stick 是最通用的加速器，可运行大多数结构。