Publication: A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
Date
2023
Authors
Madanan M.
Gunasekaran S.S.
Mahmoud M.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Image classification is a popular and important area of image processing research in today's society. For machine learning, SVM is a very good classification model. CNN is a type of convolution neural network that has an unpredictable development and uses convolution calculations. It is one of the most well-known deep learning algorithms. This review thinks about and inspects exemplary AI and profound learning picture classification procedures involving SVM and CNN as specific illustrations. Using a large sample mnist dataset, this study found that CNN has an accuracy of 0.97 and SVM has an accuracy of 0.89
SVM has an accuracy of 0.85 and CNN has an accuracy of 0.82 when working with a small sample ImageNet dataset. Tests in this review show that for little example informational collections, standard ML has an improved arrangement impact than deep learning structure does. � 2023 IEEE.
SVM has an accuracy of 0.85 and CNN has an accuracy of 0.82 when working with a small sample ImageNet dataset. Tests in this review show that for little example informational collections, standard ML has an improved arrangement impact than deep learning structure does. � 2023 IEEE.
Description
Keywords
deep learning , image classification , machine learning , Convolution , Convolutional neural networks , Deep learning , Large datasets , Learning algorithms , Learning systems , Support vector machines , Classification models , Classification procedure , Comparative analyzes , Convolution neural network , Deep learning , Images classification , Images processing , Machine-learning , Small samples , Standard ML , Image classification