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Fu, X.T., Meng, X.Y., Zhou, J. and Ji, Y. (2023) High-Risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, 5-8 December 2023, 1905-1910.
https://doi.org/10.1109/BIBM58861.2023.10385853

has been cited by the following article:

• 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.