Research Article
Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)
@ARTICLE{10.4108/eai.28-5-2020.166290, author={Inderpreet Singh Walia and Muskan Srivastava and Deepika Kumar and Mehar Rani and Parth Muthreja and Gaurav Mohadikar}, title={Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={6}, number={23}, publisher={EAI}, journal_a={PHAT}, year={2020}, month={9}, keywords={Pneumonia, Depth Wise Learning, X-Rays Images, Data Augmentation, CNN}, doi={10.4108/eai.28-5-2020.166290} }- Inderpreet Singh Walia
Muskan Srivastava
Deepika Kumar
Mehar Rani
Parth Muthreja
Gaurav Mohadikar
Year: 2020
Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)
PHAT
EAI
DOI: 10.4108/eai.28-5-2020.166290
Abstract
INTRODUCTION: Pneumonia is most significant disease in today’s world. It resulted around 15 % of the total deaths of children of the same age group.
OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning (VGG16) to reliably diagnose pneumonia.
METHODS: The proposed model contains 10 layers of convolutional neural networks. Also, three dense layers with the Swish activation function with a dropout of 0.7 and 0.5 respectively in each layer. The model was trained on 5216 augmented with weighted contrast and brightened radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16).
RESULT: The model was trained on 5216 augmented radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16) and the final results obtained with training accuracy of 98.5%, testing accuracy of 79.8% and validation accuracy of 75%.
CONCLUSION: The model can be improved by using different transfer learning models and hyperparameter tuning parameters.
Copyright © 2020 Inderpreet Singh Walia et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.