This research aims to investigate the classification accuracy of various state-of-the-art image classification models across different categories of breast ultrasound images, as defined by the Breast Imaging Reporting and Data System (BI-RADS). To achieve this, we have utilized a comprehensively assembled dataset of 2,945 mammographic images sourced from 1,540 patients. In order to conduct a thorough analysis, we employed six advanced classification architectures, including VGG19 \cite{simonyan2014very}, ResNet50 \cite{he2016deep}, GoogleNet \cite{szegedy2015going}, ConvNext \cite{liu2022convnet}, EfficientNet \cite{tan2019efficientnet}, and Vision Transformers (ViT) \cite{dosovitskiy2020image}, instead of traditional machine learning models. We evaluate models in three different settings: full fine-tuning, linear evaluation and training from scratch. Our findings demonstrate the effectiveness and capability of our Computer-Aided Diagnosis (CAD) system, with a remarkable accuracy of 76.39\% and an F1 score of 67.94\% in the full fine-tuning setting. Our findings indicate the potential for enhanced diagnostic accuracy in the field of breast imaging, providing a solid foundation for future endeavors aiming to improve the precision and reliability of CAD systems in medical imaging.

通过使用来自1,540名患者的2,945张乳腺超声图像，研究了不同BI-RADS定义的乳腺超声图像类别对各种最先进的图像分类模型的分类准确性。结果表明，使用多种先进的分类架构（包括VGG19、ResNet50、GoogleNet、ConvNext、EfficientNet和Vision Transformers），而非传统的机器学习模型，通过全方位的分析，我们的计算机辅助诊断(CAD)系统显著提高了准确性，在全fine-tuning设置下的准确率为76.39%和F1分数为67.94%。这些发现表明乳腺成像领域的诊断精确度有潜力得到提高，为未来旨在改善医学成像CAD系统的精确度和可靠性的努力奠定了坚实的基础。

乳腺X光摄影图像中基于BI-RADS的机器学习分类性能