Breast cancer is the second leading cause of death in females. As such, women have high incidence and mortality rates of breast cancer. The incidence rate has been on the rise over time. The earlier breast cancer is caught, the better it shows prognosis and the lower the mortality rate is. For this reason, many researchers and medical doctors have heeded a lot of attention to the CAD systems to detect and classify breast cancer. They have proposed a myriad of methods and techniques. Among them, the CAD system based on artificial intelligence (AI) can process plenty of information fast, and its performance is evaluated to be high. As an AI algorithm, YOLO has excellent detection performance and can detect objects effectively in real time. In this paper, we proposed an anomaly detection model of mammography using a YOLOv4-based histogram. In terms of breast cancer diagnosis, mammography features a fast diagnosis time and an inexpensive cost. For this reason, it is often applied to breast cancer diagnosis. Mammography, however, generates an image only with brightness values, so that a mammogram image has a lot of noise and image edges are dim. To enhance these image edges, we create a difference through histogram and brightness range control and threshold-based region removal methods and expand the single channel of mammogram images using the generated images. Through the expansion, the image edges are enhanced and converted into a single channel again and are learned through YOLO. For performance evaluation, the method proposed in this study is compared with ResNet18, ResNet50, GoogleNet, and VGG16. According to an experiment, the proposed method had the highest accuracy, or 95.74%, followed by GoogleNet (89.9%), VGG16 (88.93%), ResNet50 (87.77%), and ResNet18 (87.67%) in order.

乳腺癌是导致女性死亡的第二大原因。因此，女性的乳腺癌发病率和死亡率都很高。随着时间的推移，发病率一直在上升。发现乳腺癌越早，预后越好，死亡率越低。出于这个原因，许多研究人员和医生都非常关注 CAD 系统来检测和分类乳腺癌。他们提出了无数的方法和技术。其中，基于人工智能（AI）的CAD系统可以快速处理大量信息，其性能评价很高。YOLO作为一种AI算法，具有出色的检测性能，可以实时有效地检测物体。在本文中，我们提出了一种使用基于 YOLOv4 的直方图的乳房 X 光检查异常检测模型。在乳腺癌诊断方面，乳房X光检查具有诊断时间快、成本低廉的特点。因此，它经常被应用于乳腺癌的诊断。但是，乳房 X 光检查仅生成具有亮度值的图像，因此乳房 X 光检查图像具有大量噪声且图像边缘暗淡。为了增强这些图像边缘，我们通过直方图和亮度范围控制以及基于阈值的区域去除方法创建差异，并使用生成的图像扩展乳房 X 光图像的单通道。通过展开，图像边缘被增强，再次转化为单通道，通过YOLO学习。对于性能评估，将本研究中提出的方法与 ResNet18、ResNet50、GoogleNet 和 VGG16 进行了比较。根据实验，所提出的方法的准确率最高，为 95。