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Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy

Sudan Jha1, Sultan Ahmad2,3, Anoopa Arya4,5

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|February 10, 2023
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This summary is machine-generated.

Early breast cancer detection is crucial for women

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Oncology

Background:

  • Breast cancer is a leading cause of mortality in women, particularly in India.
  • Late detection of breast tumors often leads to poor treatment outcomes.
  • Advanced imaging techniques are vital for early disease identification.

Purpose of the Study:

  • To develop an automated method for early breast cancer detection using mammograms.
  • To improve the accuracy and efficiency of breast cancer diagnosis through image analysis.

Main Methods:

  • Hybrid segmentation techniques combining CLAHE and morphological operations on mammograms.
  • Deep learning models for classifying segmented images.
  • Utilized the Mammography Image Analysis Society (MIAS) database for model training and validation.

Main Results:

  • The study achieved high accuracy in detecting breast cancer indicators.
  • Evaluated key performance metrics including threshold, accuracy, sensitivity, and specificity.
  • Demonstrated the effectiveness of the hybrid segmentation and deep learning approach.

Conclusions:

  • The proposed hybrid segmentation and deep learning method shows promise for accurate and timely breast cancer detection.
  • This approach can aid in improving patient survival rates and treatment efficacy.
  • Highlights the potential of AI in medical diagnostics for early disease intervention.