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An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

Shazia Shamas1, Surya Narayan Panda1,*, Ishu Sharma1,*, Kalpna Guleria1, Aman Singh2,3,4, Ahmad Ali AlZubi5, Mallak Ahmad AlZubi6

1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India
2 Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, Santander, 39011, Spain
3 Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR, 00613, USA
4 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, 248007, India
5 Computer Science Department, King Saud University, Riyadh, Saudi Arabia
6 Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan

* Corresponding Authors: Surya Narayan Panda. Email: email; Ishu Sharma. Email: email

Computer Modeling in Engineering & Sciences 2024, 138(2), 1051-1075. https://doi.org/10.32604/cmes.2023.030712

Abstract

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC), K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and K-means with Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer Action Program (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics: precision, sensitivity, specificity, f-measure, accuracy, Matthews Correlation Coefficient (MCC), Jaccard, and Dice. The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancer images. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and F-measure of 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove that K-means with ABC, K-means with PSO, K-means with FFA, and K-means with CSA have achieved an improvement of 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentation for lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significant improvement in accuracy, hence can be utilized by researchers for improved segmentation processes of medical image datasets for identifying the targeted region of interest.

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APA Style
Shamas, S., Panda, S.N., Sharma, I., Guleria, K., Singh, A. et al. (2024). An improved lung cancer segmentation based on nature-inspired optimization approaches. Computer Modeling in Engineering & Sciences, 138(2), 1051-1075. https://doi.org/10.32604/cmes.2023.030712
Vancouver Style
Shamas S, Panda SN, Sharma I, Guleria K, Singh A, AlZubi AA, et al. An improved lung cancer segmentation based on nature-inspired optimization approaches. Comput Model Eng Sci. 2024;138(2):1051-1075 https://doi.org/10.32604/cmes.2023.030712
IEEE Style
S. Shamas et al., "An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches," Comput. Model. Eng. Sci., vol. 138, no. 2, pp. 1051-1075. 2024. https://doi.org/10.32604/cmes.2023.030712



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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