Treffer: Image recognition using deep learning:a review.

This paper presents a comprehensive review of recent advancements in image recognition, with a focus on deep learning (DL) techniques. Convolutional neural networks (CNNs), in particular, have significantly transformed this domain, enabling substantial improvements in both accuracy and efficiency across diverse applications. The review explores state-of-the-art methods, highlighting their practical implementations and the progress achieved. It also addresses key challenges such as data scarcity and model interpretability, offering perspectives on emerging opportunities and future directions. By synthesizing current trends with forward-looking insights, the paper aims to serve as a valuable resource for researchers and practitioners seeking to navigate and contribute to the evolving landscape of image recognition. Moreover, the paper examines critical challenges that persist in the field, such as transfer learning, data augmentation, and explainable artificial intelligence (AI) approaches. By synthesizing current trends with emerging innovations, the review not only maps the trajectory of progress but also highlights future directions and research opportunities. This synthesis aims to provide researchers, developers, and industry practitioners with a solid understanding of the dynamic and rapidly evolving environment surrounding image recognition technologies. [ABSTRACT FROM AUTHOR]

Copyright of Indonesian Journal of Electrical Engineering & Computer Science is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)