乳腺癌筛查对于降低死亡率至关重要。 YOLOv9 是一种新的实时对象检测模型，非常适合癌症筛查。定制的 YOLOv9 模型具有基于物种和形态多样性的乳腺癌检测增强功能，具有潜在的临床意义。内部数据集由 687 个病例组成，按 3:1 进行交叉验证。此外，还使用外部数据集中的 98 个案例进行测试。我们开发了一个专为乳腺癌检测定制的 FS-YOLOv9 模型，该模型在 Adown 的 Conv1 之前加入了一个额外的最大池化层，以增强高亮度特征。主干中P3的Adown被替换为高频Haar小波卷积核，在下采样时忽略低频分量以增强形态和纹理特征。我们模型的可靠性和稳健性是通过测量 F1 分数、自由反应受试者工作特征 (FAUC) 曲线下面积、平均精确度 (mAP)、召回率和精确度来确定的，并将它们与官方YOLOv9、YOLOv8、YOLOv5模型。与官方YOLOv9模型相比，FS-YOLOv9在内部数据集；在外部测试数据集中，FS-YOLOv9将平均F1分数、FAUC和mAP50分别提高了4.58%、5.78%和4.41%。我们的FS-YOLOv9模型在检测乳腺癌方面表现出显着提高的性能，使其更加实用用于高风险乳腺癌诊断。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Breast cancer screening is critical for reducing mortality rates. YOLOv9, a new real-time object-detection model, is ideal for cancer screening. A customized YOLOv9 model with enhancements for detecting breast cancer on the basis of species and morphological diversity has potential clinical significance.The internal dataset consisted of 687 cases split 3:1 for cross-validation. Additionally, 98 cases from external datasets were used for testing. We developed an FS-YOLOv9 model customized for breast cancer detection that incorporated an extra max-pooling layer before the Conv1 of the Adown to enhance high-brightness features. The Adown of the P3 in the backbone was replaced with a high-frequency Haar wavelet convolution kernel, which ignored the low-frequency components during down-sampling to enhance morphology and texture features. The reliability and robustness of our model was determined by measuring the F1 score, the area under curve of free-response receiver operating characteristic (FAUC), mean average precision (mAP), recall, and precision, and comparing them with the findings for the official YOLOv9, YOLOv8, YOLOv5 models.In comparison with the official YOLOv9 model, FS-YOLOv9 showed a higher average F1 score (0.700 vs. 0.669), FAUC (0.695 vs. 0.662), and mAP50 (0.713 vs. 0.679) in the internal dataset; in the external testing dataset, the FS-YOLOv9 improved the average F1 score, FAUC, and mAP50 by 4.58%, 5.78%, and 4.41% respectively.Our FS-YOLOv9 model showed significantly improved performance in detecting breast cancer, making it more practical for high-risk breast cancer diagnosis.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.