Version 1
: Received: 19 August 2024 / Approved: 19 August 2024 / Online: 19 August 2024 (12:20:59 CEST)
How to cite:
Mienye, I. D.; Swart, T. G.; Obaido, G.; Jordan, M.; Ilono, P. Deep Convolutional Neural Networks: A Comprehensive Review. Preprints2024, 2024081288. https://doi.org/10.20944/preprints202408.1288.v1
Mienye, I. D.; Swart, T. G.; Obaido, G.; Jordan, M.; Ilono, P. Deep Convolutional Neural Networks: A Comprehensive Review. Preprints 2024, 2024081288. https://doi.org/10.20944/preprints202408.1288.v1
Mienye, I. D.; Swart, T. G.; Obaido, G.; Jordan, M.; Ilono, P. Deep Convolutional Neural Networks: A Comprehensive Review. Preprints2024, 2024081288. https://doi.org/10.20944/preprints202408.1288.v1
APA Style
Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2024). Deep Convolutional Neural Networks: A Comprehensive Review. Preprints. https://doi.org/10.20944/preprints202408.1288.v1
Chicago/Turabian Style
Mienye, I. D., Matt Jordan and Philip Ilono. 2024 "Deep Convolutional Neural Networks: A Comprehensive Review" Preprints. https://doi.org/10.20944/preprints202408.1288.v1
Abstract
Deep convolutional neural networks (CNNs) have revolutionised computer vision technology by automatically learning hierarchical representations of image data, leading to state-of-the-art performance in visual recognition tasks. This article presents a comprehensive review of deep CNNs, from their evolution and architectures to the current state-of-the-art research. The review also provides the core concepts and building blocks of CNNs and their concise mathematical representations. Furthermore, it explores applications of deep CNNs in three popular domains, including medical diagnosis, remote sensing, and facial recognition. This review will benefit researchers and practitioners in computer vision and artificial intelligence, as well as industry professionals seeking to leverage the latest advancements in deep learning technologies.
Keywords
CNN; deep learning; facial recognition; image classification; medical imaging; neural networks; remote sensing; segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
