Author(s)

Bitasadat Jamshidi¹, Nastaran Ghorbani^2*, Mohsen Rostamy-Malkhalifeh¹

¹Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, Iran.
²Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA.

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.

Lung Cancer, CT Scan Imaging, Deep Learning, Canny Edge Detection, Wavelet Transform, Multi-Layer Perceptron, Dragonfly Algorithm

Jamshidi, B. , Ghorbani, N. and Rostamy-Malkhalifeh, M. (2025) Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA). Open Journal of Medical Imaging, 15, 106-136. doi: 10.4236/ojmi.2025.153010.