Journal Article

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Fadhel, Mohammed A., Zhang, Jinglan, Santamaría, J., Al-Amidie, Muthana, Humaidi, Amjad J., Al-Dujaili, Ayad, Alzubaidi, Laith, Farhan, Laith, Al-Shamma, Omran, Duan, Ye

2021

Overview

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

• Publisher: Springer International Publishing,Springer Nature B.V,SpringerOpen
• Subject: Application / Artificial neural networks / Big Data / Bioinformatics / Clinical information / Cognitive tasks / Communications Engineering / Computational Science and Engineering / Computer Science / Convolution neural network (CNN) / Cybersecurity / Data Mining and Knowledge Discovery / Data processing / Database Management / Deep learning / Deep learning applications / Deep neural network architectures / Holistic approach / Human performance / Image classification / Information processing / Information Storage and Retrieval / Machine learning / Mathematical Applications in Computer Science / Medical informatics / Natural language processing / Networks / Neural networks / Robotics / Robots / Software / State-of-the-art reviews / Survey Paper / Task complexity / Task performance