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Related Experiment Video

Updated: Sep 16, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.

Afnan M Alhassan1, Nouf I Altmami1

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.

Plos One
|July 10, 2025
PubMed
Summary

Accurate mitosis detection in cancer diagnosis is improved by a Customized Deep Learning (CDL) model. This approach uses transfer learning and optimization algorithms to enhance accuracy and localization for better cancer assessment.

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Area of Science:

  • Pathology
  • Computational Biology
  • Machine Learning

Background:

  • Accurate mitosis identification is vital for cancer diagnosis.
  • Challenges include class imbalance and morphological variations in histopathological images.
  • Existing methods struggle with precise mitosis detection.

Purpose of the Study:

  • To develop a Customized Deep Learning (CDL) model for enhanced mitosis detection in histopathological images.
  • To address challenges of class imbalance and improve localization accuracy.
  • To leverage transfer learning and hybrid optimization algorithms for superior performance.

Main Methods:

  • A Customized Deep Learning (CDL) model integrating transfer learning and hybrid CNN architecture.
  • Utilized skip connections for improved mitosis localization.
  • Employed a hybrid optimization mechanism combining Jellyfish Search Optimizer (JSO) and Walrus Optimization Algorithm (WOA).

Main Results:

  • The CDL model achieved an F1 score of 0.994 and 98.8% accuracy on benchmark datasets.
  • Demonstrated superior performance in detecting mitotic figures.
  • Effectively countered class imbalance and improved feature extraction.

Conclusions:

  • The CDL model offers a robust and highly effective solution for mitosis detection.
  • Significantly aids pathologists in accurate cancer diagnosis and prognosis.
  • Shows potential for real-time applications and broader histopathological analyses.