Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)

• URL: http://arxiv.org/abs/2408.15355v1
• Date: Tue, 27 Aug 2024 18:27:47 GMT
• 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,
• Abstract summary: 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. 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.
• Score: 0.294944680995069
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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