Aux-MVNet: Auxiliary Classifier-Based Multi-View Convolutional Neural Network for Maxillary Sinusitis Diagnosis on Paranasal Sinuses View

Abstract

Computed tomography (CT) is undoubtedly the most reliable and the only method for accurate diagnosis of sinusitis, while X-ray has long been used as the first imaging technique for early detection of sinusitis symptoms. More importantly, radiography plays a key role in determining whether or not a CT examination should be performed for further evaluation. In order to simplify the diagnostic process of paranasal sinus view and moreover to avoid the use of CT scans which have disadvantages such as high radiation dose, high cost, and high time consumption, this paper proposed a multi-view CNN able to faithfully estimate the severity of sinusitis. In this study, a multi-view convolutional neural network (CNN) is proposed which is able to accurately estimate the severity of sinusitis by analyzing only radiographs consisting of Waters' view and Caldwell's view without the aid of CT scans. The proposed network is designed as a cascaded architecture, and can simultaneously provide decisions for maxillary sinus localization and sinusitis classification. We obtained an average area under the curve (AUC) of 0.722 for maxillary sinusitis classification, and an AUC of 0.750 and 0.700 for the left and right maxillary sinusitis, respectively, using the proposed network.