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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
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Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.

Selvakumar Thirumalaisamy1, Kamaleshwar Thangavilou2, Hariharan Rajadurai3

  • 1Department of Artificial intelligence & Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.

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Summary
This summary is machine-generated.

This study introduces EACO-ResNet101, a novel deep learning model for breast cancer detection. It significantly improves accuracy in classifying breast cancer from mammograms, aiding radiologists in early anomaly identification.

Keywords:
Ant Colony OptimizationResNet101breast cancerconvolutional neural networkhyperparameterstransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer remains a leading cause of mortality in women, underscoring the need for early and accurate detection.
  • Traditional methods for breast cancer detection and classification have limitations.
  • Convolutional Neural Networks (CNNs) show promise in enhancing medical image analysis for tumor identification.

Purpose of the Study:

  • To develop a comprehensive classification technique for breast cancer detection using a synthesized CNN and an enhanced optimization algorithm.
  • To assist radiologists in the rapid and accurate identification of breast cancer anomalies.
  • To improve the accuracy, sensitivity, and specificity of breast cancer classification in mammographic datasets.

Main Methods:

  • A novel Enhanced Ant Colony Optimization (EACO) algorithm, modified with opposition-based learning (OBL), was developed to optimize CNN hyperparameters.
  • The EACO algorithm was integrated with the Residual Network-101 (ResNet101) CNN architecture, creating the EACO-ResNet101 model.
  • The proposed model was evaluated on the MIAS and CBIS-DDSM mammographic datasets.

Main Results:

  • The EACO-ResNet101 model achieved high performance on the CBIS-DDSM dataset, with 98.63% accuracy, 98.76% sensitivity, and 98.89% specificity.
  • On the MIAS dataset, the model demonstrated 99.15% accuracy, 97.86% sensitivity, and 98.88% specificity.
  • The proposed model significantly outperformed conventional methods in breast cancer classification.

Conclusions:

  • The developed EACO-ResNet101 model offers a superior approach for breast cancer classification in mammography.
  • This AI-driven technique has the potential to enhance diagnostic accuracy and support clinical decision-making for radiologists.
  • The findings highlight the effectiveness of combining advanced optimization algorithms with deep learning for medical image analysis.