Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)
<|reference_start|>Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA): Lung cancer stands as the preeminent cause of cancer-related mortality globally. Prompt and precise diagnosis, coupled with effective treatment, is imperative to reduce the fatality rates associated with this formidable disease. This study introduces a cutting-edge deep learning framework for the classification of lung cancer from CT scan imagery. The research encompasses a suite of image pre-processing strategies, notably Canny edge detection, and wavelet transformations, which precede the extraction of salient features and subsequent classification via a Multi-Layer Perceptron (MLP). The optimization process is further refined using the Dragonfly Algorithm (DA). The methodology put forth has attained an impressive training and testing accuracy of 99.82\%, underscoring its efficacy and reliability in the accurate diagnosis of lung cancer.<|reference_end|>
@article{jamshidi2024optimizing,
    title={Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer
  Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)},
    author={Bitasadat Jamshidi, Nastaran Ghorbani, Mohsen Rostamy-Malkhalifeh},
    journal={arXiv preprint arXiv:2408.15355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15355},
    primaryClass={eess.IV cs.CV cs.LG}
}