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Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations.

Awika Ariyametkul1, May Phu Paing1

  • 1Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.

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Summary

This study enhanced breast cancer detection using YOLO11 and explainable AI (XAI). YOLO11 achieved high accuracy, while HiResCAM provided clear visual explanations, improving AI trustworthiness in medical imaging.

Keywords:
Breast cancerYou Only Look Once (YOLO)explainable artificial intelligence (XAI)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of mortality in women, necessitating early detection through mammography.
  • Artificial intelligence (AI), particularly You Only Look Once (YOLO) models, shows promise in medical image analysis.
  • Current AI models often lack transparency, hindering trustworthiness in clinical applications.

Purpose of the Study:

  • To evaluate the performance of YOLOv9, YOLOv10, and YOLO11 for breast cancer detection using INbreast and MIAS datasets.
  • To integrate and assess seven explainable artificial intelligence (XAI) methods with the best-performing YOLO model.
  • To enhance the transparency and trustworthiness of AI-driven breast cancer detection.

Main Methods:

  • Trained YOLOv9, YOLOv10, and YOLO11 on INbreast and MIAS mammography datasets.
  • Selected the top-performing YOLO model based on mean average precision (mAP), precision, and recall.
  • Integrated seven XAI methods (Grad-CAM, Grad-CAM++, Eigen-CAM, EigenGrad-CAM, XGrad-CAM, LayerCAM, HiResCAM) for model interpretability.

Main Results:

  • YOLO11 achieved the highest mAP (0.935), outperforming YOLOv9 (0.868) and YOLOv10 (0.926).
  • YOLO11 demonstrated classification accuracies of 95% for benign and 80% for malignant cases.
  • HiResCAM provided the most effective visual explanations, achieving the highest matching ground truth (mGT) score of 0.49.

Conclusions:

  • The combination of YOLO11 and HiResCAM offers high accuracy and improved interpretability for breast cancer detection.
  • This integrated approach enhances user trust by visualizing AI decision-making processes.
  • The findings provide insights for refining AI models and improving their performance in medical diagnostics.