Automatique breast cancer CAD

Abstract

Breast cancer is still one of the most frequent malignancies in the world, having claimed the lives of millions of people. Clinical specialists identify suspicious lumps to help diagnose breast problems. Due to the large number of mammograms and the time and effort required to study each view of a mammogram, this activity poses a daily challenge for radiologists. The failure of mammography to detect abnormalities is increasing due to poor image quality, eye tiredness, or radiologists’ negligence. Breast cancer identification at an early stage is crucial for lowering the mortality rate among women. That’s why, in order to construct an autonomous system that aids radiologists in diagnosing breast cancer, we employed YOLO (a real-time object identification algorithm) and VGG16 to detect and pinpoint malignant lesions on mammograms.