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Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning.

Tengku Muhammad Hanis1, Nur Intan Raihana Ruhaiyem2, Wan Nor Arifin3

  • 1Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia.

Diagnostics (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an AI tool using ensemble transfer learning and digital mammograms to aid radiologists in breast cancer risk estimation. The model shows promise in improving diagnostic efficiency and reducing radiologist workload.

Keywords:
Asian womenartificial intelligencebreast cancerdeep learningdiagnostic screeningmammographyradiologiststransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading global health concern.
  • Improving the efficiency of breast cancer diagnosis is crucial.
  • Radiologists require advanced tools for accurate and timely detection.

Purpose of the Study:

  • To develop a supplementary diagnostic tool for radiologists.
  • To enhance breast cancer risk estimation using ensemble transfer learning.
  • To improve the medical workflow for breast cancer screening and diagnosis.

Main Methods:

  • Utilized digital mammograms and associated data from Hospital Universiti Sains Malaysia.
  • Evaluated thirteen pre-trained deep learning networks.
  • Developed three ensemble models based on performance metrics (PR-AUC, precision, F1 score).

Main Results:

  • ResNet101, ResNet152, and ResNet50V2 formed the final ensemble model.
  • The final model achieved a mean precision of 0.82, F1 score of 0.68, and Youden J index of 0.12.
  • The model demonstrated balanced performance across varying mammographic densities.

Conclusions:

  • Ensemble transfer learning with digital mammograms is effective for breast cancer risk estimation.
  • The developed model can serve as a valuable supplementary diagnostic tool for radiologists.
  • This AI tool has the potential to reduce radiologist workload and optimize breast cancer diagnostic workflows.