A Multiple Deep Learner Approach for X-Ray Image-Based Pneumonia Detection

Abstract

Pneumonia is a lung disease caused by bacterial or viral infection. Early diagnosis is an important factor for successful treatment. In this study, we use three well-known convolutional neural network models, namely Faster RCNN ResNet-101, Mask RCNN ResNet-101, and Mask RCNN ResNet-50 for detection of pneumonia. We use data augmentation, transfer learning and fine-tuning in the training stage. Experimental results show that different networks have different characteristics on the same dataset. Therefore, we propose a multiple deep learner approach to improve the prediction performance via combination of different object detection models. As a result, the proposed approach can find more opacity areas of the lungs where the early symptoms are not evident. While maintaining the prediction accuracy, the proposed method can predict the bounding box size more precisely with a higher confidence score.