Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing

This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonstrates superior performance in medical image classification with an accuracy of 92.5%, showcasing its ability to achieve high precision and recall rates critical for medical diagnostics. GA proves effective for satellite imagery with an accuracy of 87.3%, while the CNN + Feature Fusion Network excels in MRI scans with 89.8% accuracy. Lasso Regression, though slightly less accurate at 85.6%, efficiently selects features for microscopy images within a shorter computational time. These findings highlight the strengths and trade-offs of each algorithm across different image processing domains, providing insights for selecting appropriate feature selection methods tailored to specific imaging applications.
Keywords: Feature Selection; Classification; Annealing; Multi-Modal Image; Optimization; Image Processing.
Citation : A.B. Hajira Be “Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing”, Journal of Computer Allied Intelligence (JCAI), vol.02, no.03, pp.55-66,2024.