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Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network

I Jasmine Selvakumari Jeya1, S N Deepa2

  • 1Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu 641 032, India.

Computational and Mathematical Methods in Medicine
|January 5, 2017
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Summary
This summary is machine-generated.

A novel Real Coded Genetic Algorithm (RCGA) optimizes a Radial Basis Function Neural Network (RBFNN) for accurate lung cancer image classification. This approach effectively distinguishes between healthy and cancerous lung tissues, improving diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Accurate classification of lung images is crucial for early cancer detection.
  • Existing methods face challenges like Hamming Cliff problems and local minima in classifier training.
  • Radial Basis Function Neural Networks (RBFNNs) offer effective learning but require robust optimization.

Purpose of the Study:

  • To propose and evaluate a Real Coded Genetic Algorithm (RCGA) for optimizing Radial Basis Function Neural Network (RBFNN) classifiers.
  • To enhance the classification accuracy of healthy versus cancer-affected lung images.
  • To address limitations of Binary Coded Genetic Algorithm (BCGA) by employing RCGA.

Main Methods:

  • A Real Coded Genetic Algorithm (RCGA) was developed to tune the weights and bias of an RBFNN classifier.
  • The RCGA was designed to overcome the Hamming Cliff problem inherent in BCGA.
  • The RBFNN classifier utilized a Gaussian Kernel function for efficient learning and convergence.

Main Results:

  • The proposed RCGA-based RBFNN classifier demonstrated effective classification of lung images from the LIDC and real-time databases.
  • The algorithm successfully minimized Mean Square Error (MSE) by optimizing RBFNN parameters.
  • The classification accuracy achieved was superior to previously reported methods in the literature.

Conclusions:

  • The RCGA-based RBFNN classifier provides a powerful tool for accurate lung cancer detection from medical images.
  • This hybrid approach offers improved performance and robustness in image classification tasks.
  • The study highlights the potential of advanced evolutionary algorithms in medical diagnostics.