A Novel Approach to Lung Cancer Detection Using Gorilla Troops Optimization and Convolutional Neural Networks

DOI

10.2991/978-94-6463-858-5_174How to use a DOI?

Keywords

Lung Cancer Detection; Convolutional Neural Networks; Gorilla Troops Optimization; Deep Learning; Medical Imaging; Feature Extraction

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating advanced diagnostic tools that provide high accuracy and efficiency. This paper introduces a novel framework, the “Gorilla Troops Optimization-based Convolutional Neural Network (GTO-CNN) for Lung Cancer Detection,” which synergizes deep learning with an innovative bio-inspired optimization algorithm. The proposed method leverages Convolutional Neural Networks (CNNs) for effective feature extraction from lung imaging modalities such as CT scans and X-rays. To address the challenges of feature redundancy and suboptimal parameter settings inherent in traditional methods, the Gorilla Troops Optimization (GTO) algorithm is integrated to optimize feature selection and fine-tune network parameters. The GTO algorithm, inspired by the social behavior and foraging strategies of gorilla troops, enhances convergence speed and reduces the number of iterations required to achieve optimal results. Comparative analyses against conventional optimization techniques, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—demonstrate that the proposed GTO-CNN method outperforms these benchmarks across key performance metrics. Specifically, the GTO-CNN exhibits lower convergence time (140 ms), fewer iterations (35), and superior classification performance, with an accuracy of 91.0%, precision of 89.5%, recall of 90.0%, F1 score of 90.2, and an AUC of 0.95. The experimental results underscore the potential of the GTO-CNN framework in delivering reliable and rapid lung cancer detection. Its modular design allows for integration with advanced imaging techniques and transfer learning models, ensuring adaptability to evolving clinical requirements. This work contributes a promising direction for enhancing diagnostic accuracy in medical imaging, paving the way for more effective and timely interventions in lung cancer treatment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Sujitha
AU  - M. Abimanyu
AU  - R. Abinash
AU  - S. Inbarasan
AU  - M. Manoranjan
PY  - 2025
DA  - 2025/11/04
TI  - A Novel Approach to Lung Cancer Detection Using Gorilla Troops Optimization and Convolutional Neural Networks
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2081
EP  - 2096
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_174
DO  - 10.2991/978-94-6463-858-5_174
ID  - Sujitha2025
ER  -