Breast Masses Detection Using YOLOv8

Abstract

Breast cancer stands as a formidable global health challenge, necessitating swift and precise diagnostic measures to combat its devastating impact. In this study, we delve into the efficacy of YOLOv8, a cutting-edge artificial intelligence model, for the precise detection and localizing of breast masses in digital mammography images. YOLOv8’s inherent capability to simultaneously detect and localize masses showcases accurate pinpointing of the exact locations of abnormalities within mammographic scans. Our comprehensive evaluation reveals compelling performance metrics, including an F1 score of 0.91 and a mean Average Precision (mAP) of 0.942. These results depict the robustness of the YOLOv8 in mass detection but also show better results than the conventional clinical methods, offering higher accuracy and efficiency in the diagnostic process. This study explains the transformative potential of YOLOv8 in revolutionizing breast cancer detection paradigms, presenting a promising pathway toward enhancing early detection rates and ultimately improving patient outcomes.

abnormalities within mammographic scans. Our comprehensive evaluation reveals compelling performance metrics,

 including an F1 score of 0.91 and a mean Average Precision (mAP) of 0.942. These results depict the robustness of the 

YOLOv8 in mass detection but also show better results than the conventional clinical methods, offering higher accuracy

 and efficiency in the diagnostic process.

This study explains the transformative potential of YOLOv8 in revolutionizing breast cancer detection paradigms, presenting a promising pathway toward enhancing early detection rates and ultimately improving patient outcomes.