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.