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Updated: Oct 28, 2025

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Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Amna Ali A Mohamed1, Aybaba Hançerlioğullari2, Javad Rahebi3

  • 1Department of Material Science and Engineering, University of Kastamonu, Kastamonu 37150, Turkey.

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Summary

This study introduces an AI-driven method using convolutional neural networks (CNNs) and the Fishier Mantis Optimizer for accurate colon cancer detection. This approach improves upon traditional methods, enhancing early diagnosis and patient outcomes.

Keywords:
FMOFishier Mantis Optimizercolon cancerconvolutional neural networkmetaheuristic methods

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Bio-inspired Computing

Background:

  • Traditional colon cancer diagnosis faces accuracy and efficiency limitations, hindering early detection.
  • Developing advanced computational methods is crucial for improving diagnostic capabilities.
  • Existing deep learning models like CNNs show promise but require optimization for complex medical data.

Purpose of the Study:

  • To develop and evaluate an automated colon cancer detection system using artificial intelligence.
  • To enhance the accuracy and efficiency of colon cancer diagnosis by integrating deep learning with bio-inspired optimization.
  • To address limitations in feature extraction and classification accuracy in current diagnostic models.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for intricate feature extraction from colon cancer images.
  • Employed the Fishier Mantis Optimizer, a bio-inspired algorithm, to fine-tune CNN parameters and reduce features.
  • Compared the hybrid CNN-Fishier Mantis Optimizer model against traditional methods and other optimization techniques (e.g., Genetic Algorithms).

Main Results:

  • The CNN-Fishier Mantis Optimizer model demonstrated superior performance over traditional diagnostic approaches.
  • Achieved high diagnostic accuracy, sensitivity, and specificity in distinguishing cancerous from non-cancerous colon tissues.
  • Reported key performance metrics including 94.87% sensitivity, 96.19% specificity, 97.65% accuracy, and 96.76% F1-Score.

Conclusions:

  • The hybrid AI approach significantly advances computer-aided diagnostic tools for colon cancer.
  • This method holds substantial promise for improving early detection rates and patient prognosis.
  • Integration of bio-inspired optimization with deep learning offers a robust solution for complex medical image analysis.