Anomaly detection model of mammography using YOLOv4-based histogram

• Published in: Personal and ubiquitous computing 2023-06, Vol.27 (3), p.1233-1244
• Main Authors: Kim, Chang-Min, Chung, Kyungyong, Park, Roy C.
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Artificial intelligence; Breast cancer; Brightness; Computer Science; Diagnosis; Histograms; Image enhancement; Mammography; Medical diagnosis; Medical imaging; Mobile Computing; Mortality; Object recognition; Original Article; Performance evaluation; Personal Computing; User Interfaces and Human Computer Interaction
• ISSN: 1617-4909; 1617-4917
• DOI: 10.1007/s00779-021-01598-1

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.