Open AccessA Novel Architecture for Predicting Pneumonia Patients by using LSTM, GRU and CNN
Author(s) -
Nitin Arora,
Ahatsham,
Anupam Singh,
Vivek Shahare
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.a1353.109119
Subject(s) - recurrent neural network , computer science , convolutional neural network , deep learning , artificial intelligence , long short term memory , machine learning , pattern recognition (psychology) , artificial neural network
The Models based only on Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Dynamic Recurrent Neural Network (Dynamic RNN) are not sufficient for prediction of a pneumonia patients using image processing. The proposed network uses the properties of LSTM, GRU and Convolutional Neural Network like capacity to remember long-term memory and handling the input parameters dynamically. Investigating these networks, it is found that the proposed network have a deep insight on the medical image before it can remember then forward the necessary information. In the proposed model, the LSTM and GRU directly connected with dual 1×1 convolutional network followed by the classification layers resulting with better performance. The proposed model provides the test accuracy of 94.20% and test loss of 0.04749 when tested on dataset of pneumonia patients consisting of 2 classes (normal chest, diseased chest with pneumonia) has provided better results than LSTM, GRU, Dynamic RNN and Convolutional with LSTM in all aspects like training loss, training accuracy, testing loss and testing accuracy.