Adaptive Cheetah Optimization-Driven CNN: A Hybrid Approach for Robust Image Segmentation - Patil, Rajendra V.; Patil, Rahul Manohar; Bhadane, Deepak Yashwantrao; Poddar, Govind Mohanlal; Wakekar, Anil L.; Patil, Shravani R. (International Journal of Basic and Applied Sciences, 2025)
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.
Keywords: Computer Vision; mImage Segmentation; Convolutional Neural Network; Adaptive Cheetah Optimization; Atrous Spatial Pyramid Pooling