A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification

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.