Advancing Image Segmentation, Analysis, and Feature Extraction Techniques

This study centers on review, feature extraction, and image segmentation, integrated through the Dual-Path Convolutional Neural Network (DP-CNN). The DP-CNN is designed to accurately identify objects in images, even in the presence of intricate lines and discontinuities. The MAMAM technique efficiently prioritizes critical image regions, expediting the review process and enhancing accuracy in image analysis. Additionally, feature extraction is refined using the Adaptive Feature Selection (AFS) method, facilitating precise and adaptable image processing. Each component of this approach is systematically developed to meet the research objectives. The combined methodology demonstrates robustness and versatility, proving effective across diverse and challenging data scenarios, thereby presenting a promising solution for rapid and accurate complex image processing tasks.