Dental implant recognition and classi cation with Convolutional Neural Network

Abstract

The dental implants market was worth over USD 7,222 million in 2020, and it's pre- dicted to grow to USD 11,801 million by 2026, with a compound annual growth rate of 8.6 % over the forecast period of 2021-2026. This demonstrates that the number of dental implants will dramatically increase by 2026. These contributions will create a problem for dentists all over the globe in identifying the type of implant and getting the manufacturer's company contacts. This thesis will discuss how the presented system identi ed four types of implants with acceptable accuracies. Three CNN models used are: VGG16, Xception, and ResNet50V2 were applied with transfer learning to train the models on the implants.