Ensemble of deep capsule neural networks: an application to pediatric pneumonia prediction.

Pneumonia disease accounts for 15% of all deaths in children under the age of five and early detection of the disease significantly improves survival chances. In this work, we introduce a novel deep neural network model for evaluating pediatric pneumonia from chest radio-graph images. The proposed network is an ensemble of multiple candidate networks, each with interleaved convolutional and capsule layers. Individual networks are stitched together with dense layers and trained as a single model to minimize joint loss. The proposed approach is validated through extensive experimentation on the benchmark pneumonia dataset, and the results demonstrate that the model captures higher level abstractions as well as hidden low-level features from the input radio-graphic images. Our comparison studies reveal that the proposed model produces more generic predictions than existing approaches, with an accuracy of 94.84%. The proposed model produces better scores than the existing models and is extremely useful in assisting clinicians in pneumonia diagnosis.