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Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm.

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 summary is machine-generated.

This study introduces an advanced colon cancer diagnosis technique using convolutional neural networks and a grasshopper optimization algorithm for feature selection. The method achieves high accuracy, outperforming existing approaches for reliable colon disease detection.

Keywords:
colon disease diagnoseconvolutional neural networkgrasshopper optimization algorithmmachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Colon cancer diagnosis relies heavily on accurate image analysis.
  • Traditional feature extraction methods can be computationally intensive and may not capture all relevant information.
  • Developing robust and efficient diagnostic tools is crucial for early detection and treatment.

Purpose of the Study:

  • To propose a novel, robust method for colon cancer diagnosis.
  • To enhance diagnostic accuracy by integrating deep learning feature extraction with metaheuristic feature selection.
  • To evaluate the proposed method's performance against established techniques.

Main Methods:

  • Feature extraction using various convolutional neural networks (CNNs) including Squeezenet, Resnet-50, AlexNet, and GoogleNet.
  • Feature selection using the grasshopper optimization algorithm (GOA) to reduce dimensionality and improve efficiency.
  • Classification and evaluation using machine learning models such as decision trees and support vector machines (SVM).
  • Performance metrics included sensitivity, specificity, accuracy, precision, and F1Score.

Main Results:

  • The Squeezenet-CNN and SVM combination achieved high diagnostic performance: 99.34% sensitivity, 99.41% specificity, 99.12% accuracy, 98.91% precision, and 98.94% F1Score.
  • The proposed feature selection method significantly improved the efficiency and accuracy of colon cancer diagnosis.
  • The developed method demonstrated superior performance compared to other evaluated methods like 9-layer CNN, random forest, 7-layer CNN, and DropBlock.

Conclusions:

  • The integration of CNNs for feature extraction and GOA for feature selection provides a highly accurate and robust approach to colon cancer diagnosis.
  • The proposed method offers a significant advancement in automated colon disease detection systems.
  • This technique holds promise for improving clinical diagnostic workflows and patient outcomes.