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Summary

This study introduces an automated deep learning method for lung cancer classification, achieving 98.68% accuracy. The approach uses feature fusion and optimization to improve diagnostic efficiency and accuracy, crucial for early lung cancer detection.

Keywords:
MLPPSORDORN-XSLIC segmentationcross-validation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Lung cancer is a leading cause of mortality, necessitating early diagnosis for improved survival rates.
  • Manual histopathological analysis for lung cancer diagnosis is a time-consuming process.
  • Deep learning offers potential for enhancing the accuracy and efficiency of lung cancer diagnosis.

Purpose of the Study:

  • To develop and evaluate a deep learning-based methodology for automated lung cancer classification.
  • To improve diagnostic accuracy and efficiency through advanced feature extraction, fusion, and optimization techniques.
  • To compare the performance of different deep learning architectures and optimization algorithms in lung cancer classification.

Main Methods:

  • Image preprocessing using an adaptive fuzzy filter and segmentation via a modified simple linear iterative clustering (SLIC) algorithm.
  • Feature extraction using ResNet-50, ResNet-101, and ResNet-152 (RN-X) deep learning architectures.
  • Feature fusion using a deep-weighted averaging-based feature fusion (DWAFF) technique, followed by optimization with particle swarm optimization (PSO) and red deer optimization (RDO).
  • Classification using various machine learning models including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP), evaluated with K-fold cross-validation.

Main Results:

  • The proposed DWAFF technique combined with RDO feature selection and MLP classification achieved a highest classification accuracy of 98.68% with K=10 cross-validation.
  • ResNet-X (RN-X) fused features outperformed individual ResNet variants.
  • The integration of image segmentation and feature optimization significantly improved classification accuracy.

Conclusions:

  • The developed methodology automates lung cancer classification effectively using deep learning, feature fusion, and optimization.
  • Image segmentation and feature selection are critical for enhancing diagnostic performance and accuracy in lung cancer classification.
  • Future research may focus on further optimization strategies and the development of hybrid deep learning models for lung cancer diagnosis.