Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN) - Singh Walia, Inderpreet; Srivastava, Muskan; Kumar, Deepika; Rani, Mehar; Muthreja, Parth; Mohadikar, Gaurav (EAI Endorsed Transactions on Pervasive Health and Technology, 2020)
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.