A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Abstract

Breast cancer is one of the most common lethal cancer types in the female population globally. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-raybased digital mammography images, including left mediolateral oblique, right craniocaudal, left craniocaudal, and right mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the VinDr-Mammo full-field digital mammography dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with pre-processing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The performance results were calculated using metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.