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

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

doi:10.1186/s40537-021-00444-8

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

J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8. Epub 2021 Mar 31.

Authors

Laith Alzubaidi^{1

2}, Jinglan Zhang¹, Amjad J Humaidi³, Ayad Al-Dujaili⁴, Ye Duan⁵, Omran Al-Shamma², J Santamaría⁶, Mohammed A Fadhel⁷, Muthana Al-Amidie⁵, Laith Farhan⁸

Affiliations

¹ School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000 Australia.
² AlNidhal Campus, University of Information Technology & Communications, Baghdad, 10001 Iraq.
³ Control and Systems Engineering Department, University of Technology, Baghdad, 10001 Iraq.
⁴ Electrical Engineering Technical College, Middle Technical University, Baghdad, 10001 Iraq.
⁵ Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211 USA.
⁶ Department of Computer Science, University of Jaén, 23071 Jaén, Spain.
⁷ College of Computer Science and Information Technology, University of Sumer, Thi Qar, 64005 Iraq.
⁸ School of Engineering, Manchester Metropolitan University, Manchester, M1 5GD UK.

Abstract

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.

Keywords: Convolution neural network (CNN); Deep learning; Deep learning applications; Deep neural network architectures; FPGA; GPU; Image classification; Machine learning; Medical image analysis; Supervised learning; Transfer learning.