An End-to-end learning for classification and segmentation of breast cancer

Key Points

• High accuracy was achieved in both classification and segmentation tasks, underscoring effective machine learning deployment.
• Performance metrics revealed significant improvements over baseline methods in detecting breast cancer.
• End-to-end learning framework utilized advanced techniques for image analysis and feature extraction in the model.
• Potential to enhance diagnostic accuracy for breast cancer highlights the need for continued development in medical imaging.