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Enhanced genetic algorithm-optimized deep learning features for lung cancer classification
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DOI：10.1016/j.aej.2025.12.023.png)
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Abstract
En 中文
• Fine-tuned EfficientNet-b0 and InceptionResNet-V2 models for complementary deep feature extraction from lung CT images. • Proposed an improved Genetic Algorithm (GA) with adaptive weighting and redundancy penalty for effective feature selection. • Reduced feature dimensionality by more than 50% without sacrificing classification accuracy. • Achieved high classification accuracies of 99.50% and 99.20% on two public lung cancer datasets. • Outperformed state-of-the-art methods while reducing computational cost, making the framework suitable for real-time clinical use.
Keywords:
Classification
Lung cancer
Feature extraction
Genetic algorithm
Optimization
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