Teeth category classification via seven‐layer deep convolutional neural network with max pooling and global average pooling. Issue 4 (21st May 2019)

• Record Type: Journal Article
• Authors: Li, Zhi; Wang, Shui‐Hua; Fan, Rui‐Rui; Cao, Gang; Zhang, Yu‐Dong; Guo, Ting
• Is Part Of: International journal of imaging systems and technology. Volume 29:Issue 4(2019)
• Page Start: 577
• Page End: 583
• Publication Date: 2019-05-21
• Subjects: cone beam computed tomography -- convolutional neural network -- data augmentation -- deep convolutional neural network -- deep learning -- global average pooling -- max pooling; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22337
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Accurately classify teeth category is important in further dental diagnosis. Analyzing huge dental data, that is, identifying the teeth category, is often a hard task. Current automatic methods are based on computer vision and deep learning approaches. In this study, we aimed to classify the teeth category into four classes: incisor, canine, premolar, and molar. Cone beam computed tomography was used to collect the data. We proposed a seven‐layer deep convolutional neural network with global average pooling to identify teeth category. Data augmentation method was used to enlarge the size of training dataset. The results showed the sensitivities of incisor, canine, premolar, and molar teeth are 88%, 86%, 84%, and 90%, respectively. The average sensitivity is 87.0%. We validated max pooling gives better results than average pooling. Our method is better than three state‐of‐the‐art approaches.