Deep Convolutional Neural Network (DCNN) Models for Image Recognition: A Review
Fatin A. Hamadain
, Abdalla A. Osman , Ahmed Abdelrahman Mohamed Hamed
Image recognition, object detection, deep learning, convolutional neural network, DCNN models.
The application of the artificial intelligence technique known as deep learning, which is a form of machine learning inspired by the structure and function of the brain, has had some success in the processing and analysis of visual media. The convolutional neural networks (CNNs) are one type of deep neural network that are typically utilized for image processing, particularly for images recognition and object classification. The requirement to construct a network in which neurons in the first layer extracted local visual features and neurons in the later layers combined these features to form higher-order features served as the primary impetus for the development of CNNs. Image recognition is the process of recognizing an image and assigning it to one of a set of categories. Image recognition can also be referred to as image classification. As a result, apps and software that utilize image recognition are able to ascertain what the subject matter of a photograph is and recognize its various components. Within this scope, this study investigates, analyzes, and reviews several deep convolutions neural network (DCNN) models that are designed specifically for these kinds of tasks.
"Deep Convolutional Neural Network (DCNN) Models for Image Recognition: A Review", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 8, page no.505 - 514, August-2023, Available :https://ijsdr.org/papers/IJSDR2308073.pdf
Volume 8
Issue 8,
August-2023
Pages : 505 - 514
Paper Reg. ID: IJSDR_208184
Published Paper Id: IJSDR2308073
Downloads: 000347268
Research Area: Computer Science & Technology
Country: Ar Rass, Qassim , Saudi Arabia
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
