A Review on Neural Approaches in Image Processing Applications

Abstract

Image processing is the manipulation and analysis of a digitalized image; it especially improves the image quality. Also, it yields indispensable facts about the image processing techniques required for image enhancement, restoration, preprocessing, and segmentation. These methods help to provide earlier object detection and prevent further impacts due to segmentation and classification. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on Machine Learning (ML) in image processing review has revealed distinct methodologies with diverse frameworks utilized for object detection. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, ML approaches were compared and reported as the best model. Moreover, different kinds of datasets were used to detect the objects and unknown users or intruders. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, BRATS, and ImageNet. This article discloses the implementation capacity of distinct techniques implemented for each processing methods like supervised and unsupervised. Finally, the Naïve Bayes and LMS model achieved 100% accuracy as finest. Moreover, this technique has utilized public datasets to verify the efficiency. Hence, the overall review of this article has revealed a method for detecting images effectively.