Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network

Abstract

Objective: To analyse the automatic classification performance of a convolutional neural network (CNN), Google Inception v3, using tomographic images of odontogenic keratocysts (OKCs) and ameloblastomas (AMs).

Methods: For construction of the database, we selected axial multidetector CT images from patients with confirmed AM (n = 22) and OKC (n = 18) based on a conclusive histopathological report. The images (n = 350) were segmented manually and data augmentation algorithms were applied, totalling 2500 images. The k-fold × five cross-validation method (k = 2) was used to estimate the accuracy of the CNN model.

Results: The accuracy and standard deviation (%) of cross-validation for the five iterations performed were 90.16 ± 0.95, 91.37 ± 0.57, 91.62 ± 0.19, 92.48 ± 0.16 and 91.21 ± 0.87, respectively. A higher error rate was observed for the classification of AM images.

Conclusion: This study demonstrated a high classification accuracy of Google Inception v3 for tomographic images of OKCs and AMs. However, AMs images presented the higher error rate.

Keywords: Ameloblastoma; Artificial intelligencex; Odontogenic Cysts; Tomographyx; X-Ray computed.

Figure 1.

TCMD. Axial view. Soft tissue customized window (WL 76, WW 209); b, c and d show segmented ROIs of axial sections of TCMD obtained from different patients with Am and f, g and h show ROIs after segmentation of axial sections of TCMD obtained from different patients with OKC.

Figure 2.

Schematic representation of the 2-fold ×5 method used to promote the cross-validation procedure during the learning phase. The development data consist of 70% of the original dataset and is selected manually. The test set corresponds to 10% of the development dataset.

Figure 3.

Schematic representation of the Google Inception v3 model architecture.