Detection of pneumonia using convolutional neural networks and deep learning

Abstract

The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. The main goal of this paper is to propose a solution for the above mentioned problem, using a novel deep neural network architecture. The proposed novelty consists in the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of 5856 labeled images available at one of Kaggle’s many medical imaging challenges. The chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients, aged between one and five years, from Guangzhou Women and Children’s Medical Center, Guangzhou, China. Results achieved by our network would have placed first in the Kaggle competition with the following metrics: 97.2% accuracy, 97.3% recall, 97.4% precision and AUC=0.982, and they are competitive with current state-of-the-art solutions.