PSSO: Political Squirrel Search Optimizer-Driven Deep Learning for Severity Level Detection and Classification of Lung Cancer

Avishek Choudhury, S. Balasubramaniam, Ambala Pradeep Kumar () and Sanjay Nakharu Prasad Kumar
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Avishek Choudhury: Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV, USA
S. Balasubramaniam: Department of Futures Studies, University of Kerala, Thiruvananthapuram, Kerala, India
Ambala Pradeep Kumar: ECE Department, CMR College of Engineering and Technology, Hyderabad, Telangana, India
Sanjay Nakharu Prasad Kumar: Data Scientist, San Francisco, CA, USA

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 08, 2373-2406

Abstract: Lung cancer accounts for about 7.6 million deaths annually worldwide. Early identification of lung cancer is essential for reducing preventable deaths. In this paper, we developed a Political Squirrel Search Optimization (PSSO)-based deep learning scheme for efficacious lung cancer recognition and classification. We used Spine General Adversarial Network (Spine GAN) to segment lung lobe regions where a Deep Neuro Fuzzy Network (DNFN) classifier forecasts cancerous areas. A Deep Residual Network (DRN) is also used to determine the various cancer severity levels. The Political Optimizer (PO) and Squirrel Search Algorithm (SSA) were combined to create the newly announced PSSO method. Experimental outcomes are assessed using the dataset of images from the Lung Image Database Consortium.

Keywords: Spine general adversarial network; deep residual network; deep neuro fuzzy network; Laplacian of Gaussian; political optimizer (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622023500189

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