On Convolutional Neural Networks for Chest X-ray Classification

Pneumonia is an infectious disease accounting for one fifth of the deaths of children under five worldwide. It is also a cause for adult hospital admissions with mortality rate of almost 25% in patients over 75 years. Pneumonia is curable and mortality can be prevented if it is diagnosed. Preferred diagnostic technique is chest X-ray image examination. The lack of radiology equipment of trained clinicians reduces the chance of the majority of people to be properly diagnosed. In the paper we propose a shallow convolutional neural network architecture, further fine-tuned with chose of the Adam optimizer which is split tested in the current experimental work. We showed that the automatic detection of pneumonia in chest X-ray images is possible with accuracy higher than 90% using 3 blocks of 2 convolutional 2D layers with max-pooling for feature extraction and a flatten output block comprising two dense layers, trained over 10 epochs. Therefore, the application for CNN is a viable solution as a supplement to the decision-making process of pneumonia X-ray diagnostics.