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This study introduces a combined model for accurate breast tumor segmentation, achieving high performance metrics. The model

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Biomedical Engineering

Background:

  • Accurate breast tumor segmentation is crucial for effective cancer diagnosis and treatment planning.
  • Existing segmentation methods may struggle with the inherent variability of breast tumors.

Purpose of the Study:

  • To develop and evaluate a novel combined model for precise and efficient breast tumor segmentation.
  • To compare the proposed model's performance against existing segmentation frameworks.

Main Methods:

  • Implementation of a combined model integrating advanced segmentation techniques.
  • Comparative analysis of the model's performance using Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and sensitivity.

Main Results:

  • The combined model achieved high performance with DSC of 0.94, PPV of 0.93, and sensitivity of 0.94.
  • Demonstrated superior segmentation performance compared to other existing frameworks.
  • Accurate segmentation of breast tumors was achieved.

Conclusions:

  • The developed model accurately and efficiently segments breast tumors, adapting to their variability.
  • Potential for widespread clinical application to aid in formulating diagnosis and treatment plans for breast cancer patients.