Citation :

Raghapriya.N, Aswini.N, Savitha.G, Beschi.I.S, "Lung Cancer Classification Using Adaptive Correlation Enhanced Active Contour Model and DenseEnsembleNet," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 1, pp. 160-173, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I1P113

Abstract

This study presents a novel approach to lung cancer classification combining deep learning, feature extraction, and image processing. It enhances lung cancer images using advanced denoising techniques, Non-Local Means (NLM), Wavelet, and augments the dataset with Generative Adversarial Networks (GANs) to improve model generalization. The Region of Interest (ROI) is identified with an Adaptive Correlation-Enhanced Active Contour Model (ACE-ACM), and feature extraction using pre trained CNNs like VGGNet and ResNet. To identify key features of the hybrid optimization algorithm, Sparrow Customized Sea Lion Optimization (SLO) and Sparrow Search Algorithm (SSA). The classification utilizes DenseEnsembleNet, a combination of Optimized DenseNet and CNN, achieving a remarkable accuracy of 98.79%. By using a Python-based platform, the framework provides an innovative solution for accurate lung cancer diagnosis and efficient treatment planning. Provides an innovative solution for accurate lung cancer diagnosis and efficient treatment planning.

Keywords

Adaptive Correlation Enhanced Active Contour Model, Lung cancer, DenseEnsembleNet, Denoising techniques, Generative Adversarial Networks, Region of Interest.

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