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Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on

Rayed AlGhamdi1

  • 1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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

This study introduces a new method for automated mitotic nuclei segmentation and classification in histopathological images. The technique enhances cancer diagnosis and prognosis by accurately identifying cell division stages using deep learning and optimization algorithms.

Keywords:
computer-aided diagnosisdeep learningmetaheuristicsmitotic nuclei classificationsegmentation

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Area of Science:

  • * Computational pathology
  • * Medical image analysis
  • * Machine learning in oncology

Background:

  • * Histopathological grading is crucial for tumor assessment and personalized treatment.
  • * Mitotic nuclei classification aids in cancer diagnosis and prognosis but is challenging due to small size and varied appearance.
  • * Automated methods using Computer Vision (CV) and Machine Learning (ML) are vital for accurate mitotic nuclei identification.

Purpose of the Study:

  • * To introduce an automated technique for mitotic nuclei segmentation and classification.
  • * To enhance the accuracy of identifying cells undergoing mitosis in histopathological images.
  • * To improve cancer diagnosis and prognosis through precise mitotic nuclei detection.

Main Methods:

  • * Proposed the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique.
  • * Utilized the U-Net model for image segmentation and the Xception model for feature vector generation.
  • * Employed a deep belief network (DBN) for classification, with the chaotic butterfly optimization algorithm (CBOA) for hyperparameter tuning.

Main Results:

  • * The MNSC-CBOADL technique demonstrated superior performance in automated mitotic nuclei segmentation and classification.
  • * Validation using benchmark databases confirmed the effectiveness of the proposed system.
  • * The integrated approach of deep learning and optimization algorithms significantly improved detection accuracy.

Conclusions:

  • * The MNSC-CBOADL technique offers a robust solution for automated mitotic nuclei analysis.
  • * This method has the potential to significantly aid pathologists in cancer diagnosis and treatment planning.
  • * Advanced deep learning and optimization strategies are key to overcoming challenges in histopathological image analysis.