Deployment, feature extraction, and selection in computer vision and medical imaging

Abstract

Three critical steps in the computer vision pipeline for creating machine learning frameworks for medical image classification and other vision tasks are feature extraction, selection, and deployment. The preliminary step is to extract relevant features from the raw image data, for example, pixel values, edges, textures, color histograms, and other features. Feature extraction aims to represent image data in a format that machine learning algorithms can analyze. The next step is to choose the most pertinent and discriminative features for the task. This step can improve the model's performance by lowering the dimensionality of the feature space and removing redundant or noisy features. Methods for selecting the features include univariate statistical tests, mutual information, and genetic algorithms. Finally, the features that have been selected are used to train a machine learning model, such as a support vector machine, random forest, or deep neural network. Once trained, the model may predict new, previously unseen data. Overall, feature extraction, selection, and deployment are critical steps in the computer vision pipeline, and their appropriate execution can significantly impact the final machine learning model's performance. Finally, feature extraction, selection, and deployment are critical steps in developing effective computer vision models and contribute significantly to the overall success of the medical imaging task. A deep understanding of these steps and their interactions is required to achieve cutting-edge performance in computer vision tasks. © 2025 Elsevier Inc. All rights reserved.