Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis

Abstract

Pneumonia is a lung infection that causes 15% of childhood mortality (under 5 years old), over 800,000 children under five every year, around 2,200 every day, 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 of children between one month and 16 years old) with low quality and anatomical variability (which represents one of the biggest challenges addressed in this work). We also tested other strategies such as single CNNs trained from scratch and transfer learning using CheXNet. Our results show that our ensemble model clearly outperforms these strategies obtaining highly competitive results. Finally we confirmed the robustness of our approach using another pneumonia diagnosis dataset (Kermany et al., 2018).