| Resumen |
Abstract: Pneumonia is an infectious disease that a ects the lungs and is one of the principal causes
of death in children under five years old. The Chest X-ray images technique is one of the most used
for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in
order to provide computer-aided diagnosis by automatic classification of medical images. For its
remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are
widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities,
among others, stand out. In this paper, we present a transfer learning method that automatically
classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled
as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet
as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make
comparisons with other models, we have used four well-known performance measures, obtaining
the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve
(0.97). These positive results allow us to consider our proposal as an alternative that can be useful in
countries with a lack of equipment and specialized radiologists |
