Open AccessImage Classification using a Hybrid LSTM-CNN Deep Neural Network
Author(s) -
Aditi*,
Mayank Kumar Nagda,
E. Poovammal*
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8602.088619
Subject(s) - computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , deep learning , recurrent neural network , classifier (uml) , feature extraction , artificial neural network , contextual image classification , complement (music) , long short term memory , neocognitron , image (mathematics) , time delay neural network , machine learning , biochemistry , chemistry , complementation , phenotype , gene
This work elaborates on the integration of the rudimentary Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM), resulting in a new paradigm in the well-explored field of image classification. LSTM is one kind of Recurrent Neural Network (RNN) which has the potential to memorize long-term dependencies. It was observed that LSTMs are able to complement the feature extraction ability of CNN when used in a layered order. LSTMs have the capacity to selectively remember patterns for a long duration of time and CNNs are able to extract the important features out of it. This LSTM-CNN layered structure, when used for image classification, has an edge over conventional CNN classifier. The model which has been proposed is based on the sets of Artificial Neural Network like Recurrent and Convolutional neural network; hence this model is robust and suitable to a wide spectrum of classification tasks. To validate these results, we have tested our model on two standard datasets. The results have been compared with other classifiers to establish the significance of our proposed model.