Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia.
College of Engineering, Debre Berhan University, Debre Berhan, Ethiopia.
Sci Rep. 2024 Feb 15;14(1):3839. doi: 10.1038/s41598-023-50797-8.
Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.
在埃塞俄比亚，与其他类型的癌症相比，乳腺癌在女性中的发病率最高。不幸的是，许多病例在治疗被延迟或无法进行的阶段被发现。为了解决这个问题，基于乳房 X 光摄影的筛查被广泛认为是早期发现的有效技术。然而，乳房 X 光图像的解释需要有经验的乳房成像放射科医生，而埃塞俄比亚的这种资源有限。在这项研究中，我们开发了一种模型，以帮助放射科医生进行大规模的乳房异常筛查和患者优先级排序。我们的方法结合了基于 EfficientNet 的分类器的集合与 YOLOv5，这是一种可疑肿块检测方法，用于识别异常。YOLOv5 检测的纳入对于为分类器预测提供解释和提高敏感性至关重要，特别是当分类器未能检测到异常时。为了进一步增强筛查过程，我们还纳入了一种异常检测模型。该分类器模型的 F1 得分为 0.87，灵敏度为 0.82。加入可疑肿块检测后，敏感性提高到 0.89，尽管 F1 得分略低，为 0.79。
