TITLE:
Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)
AUTHORS:
Bitasadat Jamshidi, Nastaran Ghorbani, Mohsen Rostamy-Malkhalifeh
KEYWORDS:
Lung Cancer, CT Scan Imaging, Deep Learning, Canny Edge Detection, Wavelet Transform, Multi-Layer Perceptron, Dragonfly Algorithm
JOURNAL NAME:
Open Journal of Medical Imaging,
Vol.15 No.3,
August
25,
2025
ABSTRACT: 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.