Image Classification using a Hybrid LSTM-CNN Deep Neural Network
Aditi1, Mayank Kumar Nagda2, Poovammal E3
1Aditi*, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
2Mayank Kumar Nagda, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
3E. Poovammal*, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1342-1348 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8602088619/2019©BEIESP | DOI: 10.35940/ijeat.F8602.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Artificial Intelligence, Computer Vision, Deep Learning, Neural Networks.