Abstract
This study aims to explore and develop advanced methods for the effective detection of breast pathologies using state-of-the-art machine learning techniques, specifically YOLOv8 and Faster R-CNN. Traditional approaches to breast disease diagnosis are critically reviewed, and their effectiveness is evaluated in comparison to modern automated methods. The proposed models are applied to mammographic images to identify and categorize pathological patterns into six distinct levels, considering variations in severity and disease characteristics. This multi-level classification allows for a more precise assessment of disease progression and provides critical information for personalized treatment planning. Experimental results demonstrate that the proposed approach achieves high accuracy and fast image processing, enabling reliable and rapid detection of potential breast abnormalities. These findings suggest that machine learning algorithms can significantly enhance the diagnostic process, providing clinicians with more accurate and timely information. Furthermore, the study highlights the potential of automated detection systems to improve early diagnosis, optimize treatment strategies, and ultimately enhance patient outcomes. The results emphasize the growing role of artificial intelligence in medical imaging and its transformative impact on the future of breast disease management.