Automated Teeth Disease Classification using Deep Learning Models

Abstract

This study explores the application of deep learning models for the classification of common teeth diseases, including Calculus, Tooth Discoloration, and Caries, using JPG images of teeth. The manual diagnosis of dental diseases through visual inspection can be prone to error and variability, highlighting the need for automated solutions. In this work, we utilize three convolutional neural network (CNN) architectures—VGG16, VGG19, and ResNet50—to classify teeth diseases from a dataset of labeled teeth images. Each model was trained and evaluated using 5-fold cross-validation to ensure robust performance. Our results demonstrate that ResNet50 outperforms the other models with an accuracy of 95.23%, precision of 94.38%, recall of 92.87%, and an F1-score of 93.62%. VGG19 also shows strong performance with an accuracy of 92.41%, while VGG16 achieves an accuracy of 88.26%.

References

[1] Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science. 2018 Apr 30;48(2):114-23.

[2] Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, Hanken H, Smeets R, Beck-Broichsitter B, Rendenbach C, Lakhani K. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020 Jun 24;10(6):430.

[3] Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018 Oct 1;77:106-11.

[4] Anantharajan S, Gunasekaran S, Subramanian T, Venkatesh R. MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors. 2024 Feb 1;31:101026.

[5] Mathivanan SK, Sonaimuthu S, Murugesan S, Rajadurai H, Shivahare BD, Shah MA. Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports. 2024 Mar 27;14(1):7232.

[6] Liu X, Qu L, Xie Z, Zhao J, Shi Y, Song Z. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. BioMedical Engineering OnLine. 2024 Jun 8;23(1):52.

[7] Kharaji M, Abbasi H, Orouskhani Y, Shomalzadeh M, Kazemi F, Orouskhani M. Brain Tumor Segmentation with Advanced nnU-Net: Pediatrics and Adults Tumors. Neuroscience Informatics. 2024 Feb 22:100156.

[8] Afrazeh F, Shomalzadeh M. Revolutionizing Arthritis Care with Artificial Intelligence: A Comprehensive Review of Diagnostic, Prognostic, and Treatment Innovations. International Journal of Applied Data Science in Engineering and Health. 2024 Sep 10;1(2):7-17.

[9] Mahmoudiandehkordi S, Yeganegi M, Shomalzadeh M, Ghasemi Y, Kalatehjari M. Enhancing IVF Success: Deep Learning for Accurate Day 3 and Day 5 Embryo Detection from Microscopic Images. International Journal of Applied Data Science in Engineering and Health. 2024 Aug 14;1(1):18-25.

[10] Akhoondinasab M, Shafaei Y, Rahmani A, Keshavarz H. A Machine Learning-Based Model for Breast Volume Prediction Using Preoperative Anthropometric Measurements. Aesthetic Plastic Surgery. 2024 Feb;48(3):243-9.

[11] You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020 Dec;20:1-7.

[12] Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of dentistry. 2020 Sep 1;100:103425.

[13] Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, Muhammad M, Khurram SA. Automated feature detection in dental periapical radiographs by using deep learning. Oral surgery, oral medicine, oral pathology and oral radiology. 2021 Jun 1;131(6):711-20.

[14] Almalki YE, Din AI, Ramzan M, Irfan M, Aamir KM, Almalki A, Alotaibi S, Alaglan G, Alshamrani HA, Rahman S. Deep learning models for classification of dental diseases using orthopantomography X-ray OPG images. Sensors. 2022 Sep 28;22(19):7370.

[15] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.

[16] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-778).

[17] https://www.kaggle.com/datasets/rajapriyanshu/teeth-dataset