A comparison of conventional and deep learning methods of image classification

Maryna Dovbnych; Małgorzata Plechawska–Wójcik

doi:10.35784/jcsi.2727

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Published: Dec 30, 2021

DOI: https://doi.org/10.35784/jcsi.2727

Issue Vol. 21 (2021)

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DOI

https://doi.org/10.35784/jcsi.2727

Authors

Maryna Dovbnych

maryna.dovbnych@pollub.edu.pl

Politechnika Lubelska, Poland

Małgorzata Plechawska–Wójcik

m.plechawska@pollub.pl

Lublin University of Technology, Poland

Abstract

The aim of the research is to compare traditional and deep learning methods in image classification tasks. The conducted research experiment covers the analysis of five different models of neural networks: two models of multi–layer perceptron architecture: MLP with two hidden layers, MLP with three hidden layers; and three models of convolutional architecture: the three VGG blocks model, AlexNet and GoogLeNet. The models were tested on two different datasets: CIFAR–10 and MNIST and have been applied to the task of image classification. They were tested for classification performance, training speed, and the effect of the complexity of the dataset on the training outcome.

Keywords:

image classification, machine learning, deep learning, convolutional neural networks, multilayer perceptron

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Dovbnych, M., & Plechawska–Wójcik, M. (2021). A comparison of conventional and deep learning methods of image classification. Journal of Computer Sciences Institute, 21, 303–308. https://doi.org/10.35784/jcsi.2727

A comparison of conventional and deep learning methods of image classification

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