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Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm.

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This study introduces an optimized computer-aided detection system for lung cancer, achieving 92.27% accuracy. Early lung cancer detection is crucial for improving patient survival rates.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Lung cancer is a leading cause of cancer mortality.
  • Early diagnosis and treatment significantly improve patient outcomes.
  • Automated systems can aid in the early detection of lung cancer.

Purpose of the Study:

  • To propose an automatic and optimized computer-aided detection (CAD) system for lung cancer.
  • To enhance the accuracy and efficiency of lung cancer diagnosis through advanced algorithms.
  • To improve patient life expectancy by enabling earlier detection.

Main Methods:

  • Image preprocessing (normalization, denoising).
  • Lung area segmentation using Kapur entropy maximization and mathematical morphology.
  • Feature extraction (19 GLCM features).
  • Feature selection using Improved Thermal Exchange Optimization (ITEO).
  • Classification using an ITEO-optimized artificial neural network (ANN).

Main Results:

  • The proposed method achieved an accuracy of 92.27%.
  • The system demonstrated superior performance compared to existing approaches.
  • ITEO optimization improved accuracy and convergence in feature selection and ANN classification.

Conclusions:

  • The developed CAD system offers a highly accurate and efficient method for lung cancer detection.
  • Optimized feature selection and classification are key to the system's success.
  • This approach holds significant potential for improving early lung cancer diagnosis and patient prognosis.