An Intelligent Multi-Stage Framework for Automated Lung Cancer Detection Using Optimized Segmentation and Hybrid Feature Learning
Keywords:
Grey Wolf Optimization, Lung Cancer, Minimum Redundancy Maximum Relevance (mRMR), Salp Swarm Algorithm, Support Vector Machines (SVM), Random Forest.Abstract
The problem of computed tomography (CT) images in the early and accuratedetection of lung cancer has existed since there are low contrast areas, irregular tumor margins,and high dimensional feature representations. To address them, this paper introduces a new system of multi-stage automated lung tumor detection combining the optimal segmentation,hybrid feature extraction, and intelligent classification strategies
References
Sivasankaran, P. and Dhanaraj, K.R., 2024. Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm. Revue d'Intelligence Artificielle, 38(1).
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Published
2024-09-05
How to Cite
Jaya Shrivastav,Dr. Ranu Pandey. (2024). An Intelligent Multi-Stage Framework for Automated Lung Cancer Detection Using Optimized Segmentation and Hybrid Feature Learning. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7505–7516. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4716
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