Adaptive Cheetah Optimization-Driven CNN: A Hybrid Approach for Robust Image Segmentation

  • Authors

    • Rajendra V. Patil Department of Computer Engineering, SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS), India‎ https://orcid.org/0009-0000-1105-0423
    • Rahul Manohar Patil Department of E and TC, NES’s Gangamai College of Engineering. Nagaon, Dhule, Maharashtra, India https://orcid.org/0009-0003-5579-1129
    • Deepak Yashwantrao Bhadane Department of Computer Engineering, R. C. Patel College of Engineering and Polytechnic Shirpur. Maharashtra, India https://orcid.org/0009-0002-8487-4313
    • Govind Mohanlal Poddar Department of Computer Engineering, NES’s Gangamai College of Engineering. Nagaon, Dhule, Maharashtra, India‎ https://orcid.org/0009-0007-7185-0001
    • Anil L. Wakekar Department of Computer Engineering, Devi Mahalakshmi College of Engineering & Technology, Mhaskal, Titwala (MS). India
    • Shravani R. Patil Department of Computer Engineering, SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS), India
    https://doi.org/10.14419/c62avh78

    Received date: May 26, 2025

    Accepted date: July 4, 2025

    Published date: July 9, 2025

  • Computer Vision;mImage Segmentation; Convolutional Neural Network; Adaptive Cheetah Optimization; Atrous Spatial Pyramid Pooling

  • Abstract

    Image segmentation plays a crucial role in various computer vision applications, including medical imaging, autonomous driving, and ‎remote sensing. However, achieving high segmentation accuracy remains a challenge due to issues such as noise, occlusion, complex ‎backgrounds, and class imbalance. Traditional segmentation methods often struggle with these challenges, necessitating the development ‎of advanced deep learning-based approaches. To address these issues, we propose a CNN hybrid with Adaptive Cheetah Optimization ‎‎(ACO) for improved image segmentation. The Convolutional Neural Network (CNN) serves as the backbone for feature extraction, ‎while ACO optimizes the hyperparameters and fine-tunes the segmentation process to enhance accuracy and robustness. ACO, inspired ‎by the hunting behavior of cheetahs, dynamically balances exploration and exploitation to avoid local optima and improve convergence ‎speed. Experimental results demonstrate that our hybrid approach outperforms conventional CNN-based segmentation models in terms ‎of precision, recall, and segmentation accuracy, particularly in challenging environments. This proposed method contributes to the advancement of image segmentation by addressing common challenges and improving performance through an adaptive optimization strategy‎.

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  • How to Cite

    Patil, R. V., Patil, R. M. ., Bhadane, D. Y. ., Poddar, G. M. ., Wakekar, A. L. ., & Patil, S. R. . (2025). Adaptive Cheetah Optimization-Driven CNN: A Hybrid Approach for Robust Image Segmentation. International Journal of Basic and Applied Sciences, 14(2), 691-702. https://doi.org/10.14419/c62avh78