  Pneumonia is a lung infection that causes 15% of childhood mortality, over
800,000 children under five every year, all over the world. This pathology is
mainly caused by viruses or bacteria. X-rays imaging analysis is one of the
most used methods for pneumonia diagnosis. These clinical images can be
analyzed using machine learning methods such as convolutional neural networks
(CNN), which learn to extract critical features for the classification.
However, the usability of these systems is limited in medicine due to the lack
of interpretability, because of these models cannot be used to generate an
understandable explanation (from a human-based perspective), about how they
have reached those results. Another problem that difficults the impact of this
technology is the limited amount of labeled data in many medicine domains. The
main contributions of this work are two fold: the first one is the design of a
new explainable artificial intelligence (XAI) technique based on combining the
individual heatmaps obtained from each model in the ensemble. This allows to
overcome the explainability and interpretability problems of the CNN "black
boxes", highlighting those areas of the image which are more relevant to
generate the classification. The second one is the development of new ensemble
deep learning models to classify chest X-rays that allow highly competitive
results using small datasets for training. We tested our ensemble model using a
small dataset of pediatric X-rays (950 samples) with low quality and anatomical
variability (which represents one of the biggest challenges). We also tested
other strategies such as single CNNs trained from scratch and transfer learning
using CheXNet. Our results show that our ensemble model outperforms these
strategies obtaining highly competitive results. Finally, we confirmed the
robustness of our approach using another pneumonia diagnosis dataset [1].
