Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions

Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided...

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Veröffentlicht in:Computation Jg. 11; H. 3; S. 52
1. Verfasser: Taye, Mohammad Mustafa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2023
Schlagworte:
Algorithms
Artificial intelligence
artificial intelligence (AI)
Artificial neural networks
Brain
Computer vision
convolution neural network (CNN)
Deep learning
deep learning (DL)
deep learning applications
Face recognition
Feature maps
Image classification
Image filters
Image processing
Machine learning
machine learning (ML)
Neural networks
Object recognition
Object recognition (Computers)
Pattern recognition
Jordan
ISSN:2079-3197, 2079-3197
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. Input for convolutional neural networks is provided through images. Convolutional neural networks are used to automatically learn a hierarchy of features that can then be utilized for classification, as opposed to manually creating features. In achieving this, a hierarchy of feature maps is constructed by iteratively convolving the input image with learned filters. Because of the hierarchical method, higher layers can learn more intricate features that are also distortion and translation invariant. The main goals of this study are to help academics understand where there are research gaps and to talk in-depth about CNN’s building blocks, their roles, and other vital issues.
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ISSN:2079-3197
2079-3197
DOI:10.3390/computation11030052