Deep Learning for Image Classification: A Review

Abstract

In the discipline of image processing, a variety of techniques are used to achieve various objectives, such as feature extraction, segmentation, enhancement, denoising, and classification. By addressing the possibilities and problems presented by distinct facets of image analysis and modification, these methods as a whole enable applications in a multitude of domains. All of these approaches help us better comprehend pictures, retrieve pertinent information, and make defensible judgements using visual data. Deep learning (DL) models are able to automatically extract complex characteristics that conventional approaches can overlook since they learn feature representations straight from data. Deep learning has contributed to the progress of image classification, which has been a widely pursued field of research for a considerable amount of time. This study proposed a deep learning image classification model to provide a foundation and support for image classification and massive dataset identification. Deep learning performs well in problems involving picture categorisation. However, the need for a substantial quantity of data for the training process is growing as the deep learning model structure becomes deeper and more complicated.