Deep neural network ensemble for pneumonia localization from a large scale chest x ray database
Abstract Pneumonia is a bacterial, viral, or fungal infection of one or both sides of the lungs that causes lung alveoli to fill up with fluid or pus, which is usually diagnosed with chest x rays. This work investigates opportunities for applying machine learning solutions for automated detection and localization of pneumonia on chest x ray images. We propose an ensemble of two convolutional neural networks, namely RetinaNet and Mask R CNN for pneumonia detection and localization. We validated our solution on a recently released dataset of 26,684 images from Kaggle Pneumonia Detection Challenge and were score among the top 3% of submitted solutions. With 0.793 recall, we developed a reliable solution for automated pneumonia diagnosis and validated it on the largest clinical database publicity available to date. Some of the challenging cases were additionally examined by a team of physicians, who helped us to interpret the obtained results and confirm their practical applicability.
Comput. Electr. Eng.