Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images
Abstract
According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of the tongue and teeth. Nevertheless, no study has classified tongue and dental diseases using merged datasets as of yet. This research addresses this gap by focusing on the classification of dental and tongue diseases using transfer learning techniques with CNN architecture models VGG16, VGG19, and ResNet50. The primary aim is to compare these three models to identify the one with the most optimal performance in handling related cases. Based on the results, the best accuracy was achieved with data augmentation and models trained for 75 epochs. The VGG16 model attained 94% accuracy, VGG19 achieved 93% accuracy, and ResNet50 also reached 94% accuracy. These findings suggest that transfer learning with CNN architectures can effectively classify dental and tongue diseases. The implications are significant for developing automated diagnostic tools that can aid in the early detection and treatment of oral health issues globally.
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V. Ranganathan and C. Akhila, “Streptococcus mutans: has it become prime perpetrator for oral manifestations?,” J. Microbiol. Exp., vol. 7, no. 4, pp. 206–213, 2019.
M. Sotozono et al., “Impact of sleep on the microbiome of oral biofilms,” PLoS One, vol. 16, no. 12 December, 2021.
M. A. R. Buzalaf et al., “Saliva as a diagnostic tool for dental caries, periodontal disease and cancer: is there a need for more biomarkers?,” Expert Rev. Mol. Diagn., vol. 20, no. 5, pp. 543–555, 2020.
A. M. Agbor and Y. F. J. Jupkwo, “Oral Health of Tobacco and Non-Tobacco Consumers Inyaounde, Cameroon,” Eur. J. Dent. Oral Heal., vol. 1, no. 2, 2020.
F. della Vella et al., “The pseudolesions of the oral mucosa: Differential diagnosis and related systemic conditions,” Appl. Sci., vol. 9, no. 12, pp. 1–8, 2019.
D. C. Experience, A. Factors, and A. Bahar, “Journal of International Dental and Medical Research ISSN 1309-100X http://www.jidmr.com Dental Caries Experience and Associated Factors Armasastra Bahar and et al,” pp. 666–670, 2021.
W. Qiu et al., “Application-of-AntibioticsAntimicrobial-Agents-on-Dental-CariesBioMed-Research-International.pdf,” Biomed Res. Int., vol. 20, no. 20, pp. 1–11, 2020.
C. M. A. Santoso, T. Bramantoro, M. C. Nguyen, Z. Bagoly, and A. Nagy, “Factors affecting dental service utilisation in indonesia: A population-based multilevel analysis,” Int. J. Environ. Res. Public Health, vol. 17, no. 15, pp. 1–11, 2020.
A. Riolina, S. Hartini, and S. Suparyati, “Dental and oral health problems in elementary school children: A scoping review,” Pediatr. Dent. J., vol. 30, no. 2, pp. 106–114, 2020.
I. Dewanto, S. Koontongkaew, and N. Widyanti, “Characteristics of Dental Services in Rural, Suburban, and Urban Areas Upon the Implementation of Indonesia National Health Insurance,” Front. Public Heal., vol. 8, no. May, pp. 1–8, 2020.
D. Saini, R. Jain, and A. Thakur, “Dental Caries early detection using Convolutional Neural Network for Tele dentistry,” 2021 7th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2021, pp. 958–963, 2021.
G. Alotaibi, M. Awawdeh, F. F. Farook, and M. Aljohani, “Artificial intelligence ( AI ) diagnostic tools : utilizing a convolutional neural network ( CNN ) to assess periodontal bone level radiographically — a retrospective study,” pp. 1–7, 2022.
M. Moran, M. Faria, G. Giraldi, L. Bastos, L. Oliveira, and A. Conci, “Classification of approximal caries in bitewing radiographs using convolutional neural networks,” Sensors, vol. 21, no. 15, pp. 1–12, 2021.
E. Y. Park, S. Jeong, S. Kang, J. Cho, J. Cho, and E. Kim, “Tooth caries classification with quantitative light-induced fluorescence ( QLF ) images using convolutional neural network for permanent teeth in vivo,” pp. 1–8, 2023.
S. A. Prajapati, R. Nagaraj, and S. Mitra, “Classification of Dental Diseases U sing CNN and Transfer Learning Shreyansh,” Proc. - 6th Int. Symp. Comput. Bus. Intell. ISCBI 2018, 2018.
S. Sajid, “Oral Diseases,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/salmansajid05/oral-diseases.
Tongue Classification V2, “fissured tongue Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/tongue-classification-v2/fissured-tongue.
Makhluk, “Black Hairy Tongue Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/makhluk/black-hairy-tongue.
T. C. V2, “2black Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/tongue-classification-v2/2black.
J. Dabass, “Tongue-coating,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/jyotidabas/tongue-coating/data.
T. Towfiq and Geeksforkicks, “Tongue__DIABETES,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/towfiqtomal/tongue-diabetes.
Skripsi191402083, “Diabetes Identification by Tongue Images Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/skripsi191402083/diabetes-identification-by-tongue-images.
G. Tongue, “geographic tongue Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/gepgraphic-tongue/geographic-tongue.
Raviteja, “tongue2,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/ravitejarj25/tongue2.
37, “舌像 Dataset,” Roboflow, 2023. [Online]. Available: https://universe.roboflow.com/37/-tt3dc.
C. Shorten, T. M. Khoshgoftaar, and B. Furht, Text Data Augmentation for Deep Learning, vol. 8, no. 1. Springer International Publishing, 2021.
M. Temraz and M. T. Keane, “Solving the class imbalance problem using a counterfactual method for data augmentation,” Mach. Learn. with Appl., vol. 9, no. June, p. 100375, 2022.
Jalu Nusantoro, Faldo Fajri Afrinanto, Wana Salam Labibah, Zamah Sari, and Yufis Azhar, “Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and Transfer Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 430–441, 2022.
D. Bhatt, C. Patel, H. Talsania, J. Patel, R. Vaghela, and S. Pandya, “Cnn7.Pdf,” pp. 1–28, 2021.
A. A. Reshi et al., “An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification,” Complexity, vol. 2021, 2021.
P. Sun et al., “DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system,” Secur. Commun. Networks, vol. 2020, 2020.
R. Yao, N. Wang, Z. Liu, P. Chen, and X. Sheng, “Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach,” 2021.
N. Ketkar and J. Moolayil, “Introduction to Machine Learning and Deep Learning,” in Deep Learning with Python, 2021.
H. Gholamalinezhad and H. Khosravi, “Pooling Methods in Deep Neural Networks, a Review,” 2020.
D. K. Baruah and K. Boruah, “Early Detection Of Canine Babesia From Red Blood Cell Images Using Deep Ensemble Learning,” J. Electron. Electromed. Eng. Med. Informatics, vol. 6, no. 4, pp. 509–523, 2024.
X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif. Intell. Rev., vol. 57, no. 4, 2024.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.
G. Sriram, T. R. G. Babu, R. Praveena, and J. V. Anand, “Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture,” MCB Mol. Cell. Biomech., vol. 19, no. 1, pp. 29–40, 2022.
S. M. Pradeep Singh, M. Shariff, D. P. Subramanyam, M. H. Varun, K. Shruthi, and A. S. Poornima, “Real Time Oral Cavity Detection Leading to Oral Cancer using CNN,” 2023 Int. Conf. Network, Multimed. Inf. Technol. NMITCON 2023, 2023.
I. Konovalenko, P. Maruschak, J. Brezinová, J. Viňáš, and J. Brezina, “Steel surface defect classification using deep residual neural network,” Metals (Basel)., vol. 10, no. 6, pp. 1–15, 2020.
A. Victor Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images,” Glob. Transitions Proc., vol. 2, no. 2, pp. 375–381, 2021.
N. Izdihar, S. B. Rahayu, and K. Venkatesan, “Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model,” Int. J. Informatics Vis., vol. 8, no. 1, pp. 326–332, 2024.
B. Mandal, A. Okeukwu, and Y. Theis, “Masked Face Recognition using ResNet-50,” 2021.
M. Vakili, M. Ghamsari, and M. Rezaei, “Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification,” 2020.
A. Churcher et al., “An experimental analysis of attack classification using machine learning in IoT networks,” Sensors (Switzerland), vol. 21, no. 2, pp. 1–32, 2021.
A. Agarwal, P. Sharma, M. Alshehri, A. A. Mohamed, and O. Alfarraj, “Classification model for accuracy and intrusion detection using machine learning approach,” PeerJ Comput. Sci., vol. 7, pp. 1–22, 2021.
I. AKSAKALLI, S. KAÇDIOĞLU, and Y. S. HANAY, “Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods,” Balk. J. Electr. Comput. Eng., vol. 9, no. 2, pp. 144–151, 2021.
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