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

• Main Authors: Madanan M., Gunasekaran S.S., Mahmoud M.A.
• Other Authors: 57203784027
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: 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; Images classification; Images processing; Machine-learning; Small samples; Standard ML; 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