Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs
Dadong Wang, Yulia Arzhaeva, Liton Devnath, Maoying Qiao, Saeed Amirgholipour, Qiyu Liao, Rhiannon McBean, James Hillhouse, Suhuai Luo, David Meredith, Katrina Newbigin, Deborah Yates
Abstract
Pneumoconiosis is an incurable respiratory disease caused by long-term inhalation of respirable dust. Due to small pneumoconiosis incidence and restrictions on sharing of patient data, the number of available pneumoconiosis X-rays is insufficient, which introduces significant challenges for training deep learning models. In this paper, we use both real and synthetic pneumoconiosis radiographs to train a cascaded machine learning framework for the automated detection of pneumoconiosis, including a machine learning based pixel classifier for lung field segmentation, and Cycle-Consistent Adversarial Networks (CycleGAN) for generating abundant lung field images for training, and a Convolutional Neural Network (CNN) based image classier. Experiments are conducted to compare the classification results from several state-of-the-art machine learning models and ours. Our proposed model outperforms the others and achieves an overall classification accuracy of 90.24%, a specificity of 88.46% and an excellent sensitivity of 93.33% for detecting pneumoconiosis.