Version 1
: Received: 5 May 2023 / Approved: 8 May 2023 / Online: 8 May 2023 (10:25:36 CEST)
How to cite:
Liao, D.; Hargreaves, C.A. Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model. Preprints2023, 2023050513. https://doi.org/10.20944/preprints202305.0513.v1
Liao, D.; Hargreaves, C.A. Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model. Preprints 2023, 2023050513. https://doi.org/10.20944/preprints202305.0513.v1
Liao, D.; Hargreaves, C.A. Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model. Preprints2023, 2023050513. https://doi.org/10.20944/preprints202305.0513.v1
APA Style
Liao, D., & Hargreaves, C.A. (2023). Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model. Preprints. https://doi.org/10.20944/preprints202305.0513.v1
Chicago/Turabian Style
Liao, D. and Carol Anne Hargreaves. 2023 "Classification of Dental Teeth X-Ray Images Using a Deep Learning CNN Model" Preprints. https://doi.org/10.20944/preprints202305.0513.v1
Abstract
Panoramic and periapical radiograph tools help dentists diagnose the most common dental diseases. Generally, dentists identify dental caries manually by inspecting X-ray images. However, due to their heavy workload, or poor image quality, dentists may sometimes overlook some unnoticeable dental caries, which may ultimately hinder the patient treatment. The purpose of this study was to develop an algorithm that classifies the teeth X-Ray images into three categories of “Normal”, “Caries”, and “Filled”. Our study used a dataset of 116 patients and 3712 single teeth images for training, validation and testing. Images were pre-processed using a sharpening filter and an intensity color map. We used a pre-trained transfer learning model, the NASNetMobile, which served as the feature extractor and the Convolutional Neural Network (CNN) model served as the classifier. The training dataset had a “Recall” of 0.92, 0.90 and 0.91 for “Normal”, “Caries” and “Filled” respectively and the test dataset had a “Recall” of 0.86, 0.81 and 0.85 for “Normal”, “Caries” and “Filled” respectively. The classification of the teeth X-Rays was successful and can be valuable for dentist as the artificial intelligence algorithm can serve as a decision support tool to aid dentists when they need to diagnose dental treatment.
Keywords
deep learning; medical imaging; clinical decision support system; Teeth X-Rays; Images; CNN model; transfer learning; NASNetMobile feature extractor; classification model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
