phat 20(23): e5

Research Article

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

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  • @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
Inderpreet Singh Walia1, Muskan Srivastava1, Deepika Kumar1,*, Mehar Rani1, Parth Muthreja1, Gaurav Mohadikar1
  • 1: Department of Computer Science & Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
*Contact email: deepika.kumar@bharatividyapeeth.edu

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.