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

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 x 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.