- Title
- Automated pneumoconiosis detection on chest X-rays using cascaded learning with real and synthetic radiographs
- Creator
- Wang, Dadong; Arzhaeva, Yulia; Devnath, Liton; Qiao, Maoying; Amirgholipour, Saeed; Liao, Qiyu; McBean, Rhiannon; Hillhouse, James; Luo, Suhuai; Meredith, David; Newbigin, Katrina; Yates, Deborah
- Relation
- 2020 Digital Image Computing: Techniques and Applications, DICTA 2020. 2020 Digital Image Computing: Techniques and Applications (DICTA) (Melbourne, Vic. 29 November-02 December, 2020)
- Publisher Link
- http://dx.doi.org/10.1109/DICTA51227.2020.9363416
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- 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.
- Subject
- pneumoconiosis; deep learning; comuter-aided diagnosis; black lung; SDG 3; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1460709
- Identifier
- uon:46036
- Identifier
- ISBN:9781728191096
- Language
- eng
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