Classification of chest radiographs using depthwise separable convolution

Authors

  • Mary Chris Roperos Go National Institute of Physics, University of the Philippines Diliman
  • Francis N. C. Paraan National Institute of Physics, University of the Philippines Diliman

Abstract

Pneumonia x-ray images were classified using a neural network with depthwise separable convolutions. Each image was divided into three vertical regions and convolutions were applied to each region independently. The training model has less training parameters than a standard convolutional neural network (CNN) so that the tendency for overfitting and overall computation time is reduced. The trained network features a relatively high precision (ratio of true and predicted positives) and a significantly shorter training time than a conventional CNN. Our preliminary results indicate that the precision of the neural network was not greatly affected by the introduction of depthwise separable convolutions.

Downloads

Published

2019-05-26

How to Cite

[1]
MCR Go and FNC Paraan, Classification of chest radiographs using depthwise separable convolution, Proceedings of the Samahang Pisika ng Pilipinas 37, SPP-2019-PA-21 (2019). URL: https://proceedings.spp-online.org/article/view/SPP-2019-PA-21.