Automated Risk Prediction by Measuring Pneumothorax Size Using Deep Learning

Abstract

This research proposed an approach which takes images as a DICOM format from the Kaggle dataset named “SIIM-ACR Pneumothorax Segmentation”. Preprocessed images were fed to popular Unet architecture with se_resnext50_32*4d architecture as backbone of the network, which could detect pneumothorax object on X-ray images as mask for semantic segmentation problem. Then, those mask images were post-processed for reducing noisy objects with a threshold value to identify the mask related to the pneumothorax region. Based on the mask images, percentage of the pneumothorax is calculated using C. Collins methods which also approximately determine the risk level of pneumothorax of a patient.