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Related Experiment Video

Updated: Oct 26, 2025

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Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images.

T Kavitha1, Paul P Mathai2, C Karthikeyan3

  • 1Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, India.

Interdisciplinary Sciences, Computational Life Sciences
|August 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an Optimal Multi-Level Thresholding-based Segmentation with Deep Learning enabled Capsule Network (OMLTS-DLCN) model for accurate breast cancer diagnosis from mammograms. The OMLTS-DLCN model achieved high accuracy, improving early detection rates.

Keywords:
Breast cancerClassificationDeep learningMammogramMedical imagingMetaheuristic algorithms

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Breast cancer is a prevalent disease globally, necessitating accurate and early detection methods.
  • Mammography is a key screening tool, but interpretation accuracy relies heavily on radiologist expertise and image quality.
  • Advancements in Deep Learning (DL) and Computer Vision offer promising solutions for automated breast cancer diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) model for breast cancer diagnosis using digital mammograms.
  • To enhance the accuracy and efficiency of breast cancer detection through an integrated segmentation and classification approach.

Main Methods:

  • The OMLTS-DLCN model incorporates Adaptive Fuzzy based median filtering (AFF) for noise reduction in mammograms.
  • Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (OKMT-SGO) is utilized for precise breast cancer segmentation.
  • A CapsNet (Capsule Network) acts as a feature extractor, coupled with a Back-Propagation Neural Network (BPNN) for classification.

Main Results:

  • The OMLTS-DLCN model demonstrated superior performance on benchmark Mini-MIAS and DDSM datasets.
  • Achieved high diagnostic accuracy rates of 98.50% on the Mini-MIAS dataset and 97.55% on the DDSM dataset.
  • The proposed method effectively segments and classifies breast cancer abnormalities.

Conclusions:

  • The OMLTS-DLCN model presents a highly effective approach for automated breast cancer diagnosis from mammograms.
  • The integration of advanced segmentation and DL techniques significantly improves diagnostic accuracy and aids in early detection.
  • This model holds potential for enhancing breast cancer screening programs and improving patient outcomes.