Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications.
**背景/目的：** 乳腺癌仍是全球女性死亡的主要原因。乳腺X线摄影是早期筛查的关键工具，但许多资源有限的医院缺乏专业的放射科医生和先进的诊断设备。基于深度学习的计算机辅助检测系统可通过自动化病灶检测与分类来协助放射科医生。本研究旨在评估多种YOLO模型及混合卷积-Transformer架构（包括YOLOv5、YOLOv8、YOLOv10、YOLOv11和实时检测Transformer）在低成本嵌入式系统上检测乳腺X线摄影病灶的性能。 **方法：** 我们开发了定制化的基于网络的标注工具以提高乳腺X线影像标注准确性，使用的数据集包含3169名泰国患者影像，并由三位放射科专家进行标注。病灶被分为六类：良性肿块、良性钙化、良性相关特征、恶性肿块、恶性钙化及恶性相关特征。 **结果：** 研究显示，YOLOv11n模型是NVIDIA Jetson Nano平台的最佳选择，其准确率达到0.86，推理速度为每秒6.16±0.31帧。与图形处理器系统的对比分析表明，Jetson Nano能以极低的成本实现相当的检测性能。 **结论：** 当前研究领域尚未将先进的YOLO版本集成到乳腺X线摄影的嵌入式部署中。本方法可为缺乏高端工作站的诊所提供筛查支持，证明了在资源有限环境中部署计算机辅助检测系统的可行性，并凸显了其在真实临床场景中的应用潜力。
Mammogram Analysis with YOLO Models on an Affordable Embedded System
肿瘤(癌症)患者之家