Enhancing Lung Cancer Detection in CT Imaging through Wavelet Multi-Layer Perceptron and Dragonfly Algorithm Optimization

Ravi M V; Rangaswamy C; Shobha B N

doi:10.3991/ijoe.v21i07.54143

Enhancing Lung Cancer Detection in CT Imaging through Wavelet Multi-Layer Perceptron and Dragonfly Algorithm Optimization

Authors

Ravi M V SJC Institute of Technology, Chickballapur, Karnataka, India; Visvesveraya Technological University, Belagavi, Karnataka, India https://orcid.org/0000-0003-4489-2277
Rangaswamy C SJC Institute of Technology, Chickballapur, Karnataka, India; Visvesveraya Technological University, Belagavi, Karnataka, India
Shobha B N Visvesveraya Technological University, Belagavi, Karnataka, India; B G S Institute of Technology, Nagamangala, Mandya, Karnataka, India

DOI:

https://doi.org/10.3991/ijoe.v21i07.54143

Keywords:

lung cancer, deep learning, Wavelet transform, Multi-layer Perceptron, Dragonfly Algorithm

Abstract

Globally, lung cancer continues to be the primary cause of cancer-related mortality. Reducing the death rates associated with this dangerous illness requires prompt, precise diagnosis and efficient treatment. An enhanced deep learning (DL) framework for lung cancer classification utilizing computed tomography (CT) scan images is presented in this paper. A multi-layer perceptron (MLP) is used for classification after a variety of picture preparation techniques, including wavelet transformations and Canny edge detection, are used to improve feature extraction. Additionally, the dragonfly algorithm (DA) is used to increase the optimization. This approach’s remarkable 98.6% accuracy rate shows how reliable and successful it is in identifying lung cancer.

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Published

2025-06-03

How to Cite

Ravi M V, C, R., & Shobha B N. (2025). Enhancing Lung Cancer Detection in CT Imaging through Wavelet Multi-Layer Perceptron and Dragonfly Algorithm Optimization. International Journal of Online and Biomedical Engineering (iJOE), 21(07), pp. 29–45. https://doi.org/10.3991/ijoe.v21i07.54143