Prediction of locations in medical images using orthogonal neural networks

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.
Highlights • The purpose of this study was to develop and present an orthogonal neural network (ONN), a new deep-learning structure for medical image localization, for the first time in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). • 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. • An 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%. The prediction accuracy of the ONN was compared favorably with that of the CNN. • 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.