A novel deep neural network model for image classification

Citations of this article
Mendeley users who have this article in their library.
Get full text


In this article, we have trained neural network based on deep learning architectures to classify images on standard Fashion-MNIST and CIFAR-10 dataset. The various CNN-based classification architecture and RNN-based classification architecture are trained as well as tested on those standard datasets. In CNN architecture, we include CNN with 1, 2 and 3 Convolutional Layer and in RNN architecture, we include Long-Short Term Memory (LSTM) with one and two LSTM layer. Our models show remarkable outcome on the standard benchmark dataset. The tested models like CNN1 show greater accuracy on the MNIST fashion dataset and CNN3, LSTM1 and LSTM2 performed better than other models on the CIFAR-10 dataset.




Karthika, N., Janet, B., & Shukla, H. (2019). A novel deep neural network model for image classification. International Journal of Engineering and Advanced Technology, 8(6), 3241–3249. https://doi.org/10.35940/ijeat.F8832.088619

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free