Prediction of locations in medical images using orthogonal neural networksopen access
- Authors
- Kim, Jong Soo; Cho, Yongil; Lim, Tae Ho
- Issue Date
- Jan-2021
- Publisher
- ELSEVIER
- Keywords
- Deep learning; Glottis; Localization; Orthogonal neural network; Pneumothorax
- Citation
- EUROPEAN JOURNAL OF RADIOLOGY OPEN, v.8, pp.1 - 6
- Indexed
- SCOPUS
- Journal Title
- EUROPEAN JOURNAL OF RADIOLOGY OPEN
- Volume
- 8
- Start Page
- 1
- End Page
- 6
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142427
- DOI
- 10.1016/j.ejro.2021.100388
- Abstract
- Background/Purpose: An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN).
Materials and methods: The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. In addition, ONN and CNN were applied to predict the location of the glottis in laryngeal images.
Results: An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved by applying ONN to detect the location of pneumothorax in chest X-rays; the ONN outperformed the CNN. By applying ONN to predict the location of the glottis in laryngeal images, we achieved the accurate prediction rate of 70.5% and the adjacent prediction rate of 20.5%.
Conclusions: This study demonstrated that an ONN can be used as a quick selection criterion to compare fully-connected small artificial neural network (ANN) models for image localization. The time it took to train an ONN was about 10% of the time using a CNN on images of a given input resolution. Our approach could accurately predict locations in medical images, reduce the time delay in diagnosing urgent diseases, and increase the effectiveness of clinical practice and patient care.
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