Pneumonia Detection with Weighted Voting Ensemble of CNN Models
There is a clear correlation between the diagnosis of pneumonia and abnormalities on CXR. This association can be trained through performing object detection by a deep neural network. Therefore, applying deep learning to the detection of pneumonia can be a very effective diagnostic solution. This paper proposes a method to detect lung opacities, which can be identified as pneumonia, on chest radiographs (CXR) using an ensemble of deep convolutional neural networks. Furthermore, this paper applied an ensemble model using Mask R CNN and RetinaNet to RSNA Pneumonia Detection Challenge on Kaggle and demonstrate that this method can effectively achieve high accuracy of prediction. Applying our voting ensemble methodologies outperformed all the individual models.
2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)