CLASSIFICATION OF DENTAL CARIES DISEASE IN TOOTH IMAGES USING A COMPARISON OF EFFICIENTNET-B0, MOBILENETV2, RESNET-50, INCEPTIONV3 ARCHITECTURES

Wahyuningsih Wahyuningsih; Gibran Satya Nugraha; Ramaditia Dwiyansaputra

doi:10.52436/1.jutif.2024.5.4.2187

Wahyuningsih Informatics Engineering, Engineering Faculty, Universitas Mataram, Indonesia
Gibran Satya Nugraha Informatics Engineering, Engineering Faculty, Universitas Mataram, Indonesia
Ramaditia Dwiyansaputra Informatics Engineering, Engineering Faculty, Universitas Mataram, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2187

Keywords: Caries, Convolutional Neural Network, Diagnosis, EfficientNet-B0, Image Classification

Abstract

Dental caries is a global metabolic disorder, influenced by complex interactions between the body and microbes, it's caused by prolonged exposure to a low pH environment, leading to demineralized carious lesions. If untreated, it can cause pain and eating difficulties, requiring emergency care and significantly impacting overall quality of life. Diagnosis process can be conducted through physical assessment and analyzing laboratory testing. Image-based artificial intelligence systems, particularly the EfficientNet-B0 model, is suggested as a resolution for classifying dental caries using tooth images. The study's goal is to assess EfficientNet-B0's performance in comparison to other CNN architectures and play a role in advancing medical image classification technology. The original dataset comprising 1554 images was initially collected. After augmentation, the dataset expanded to 6348 images. The data was then divided into three subsets of training, validation, and testing datasets with a distribution ratio of 70:15:15, respectively. From all the evaluated models, the EfficientNet-B0 demonstrated a quite commendable accuracy of 0.98% with overfitting tolerance of less than 2%. Having the same accuracy as the MobileNetV2 (0.98%). Despite its inability to exceed the accuracy achieved by ResNet-50 (0.99%), EfficientNet-B0 accomplished its accuracy level with roughly a quarter of the parameters than ResNet-50 and highger than InceptionV3 (0.97%), highlighting its efficiency in parameter utilization and computational resource management. These findings hold promise for enhancing models and guiding clinical decision-making.

Downloads

Download data is not yet available.

References

T. Zewdu, D. Abu, M. Agajie, and T. Sahilu, “Dental caries and associated factors in Ethiopia: systematic review and meta-analysis,” Environ. Health Prev. Med., vol. 26, no. 1, pp. 1–11, 2021, doi: 10.1186/s12199-021-00943-3.

P. Ahmad, A. Hussain, A. Carrasco-Labra, and W. L. Siqueira, “Salivary Proteins as Dental Caries Biomarkers: A Systematic Review,” Caries Res., vol. 56, no. 4, pp. 385–398, 2022, doi: 10.1159/000526942.

R. D. Saidjonovna, “Method For Improving The Prevention Of Dental Caries In Children Using The Device Aerodent,” Web Sci. Int. …, vol. 2, no. 2, pp. 36–40, 2021, [Online]. Available: https://wos.academiascience.org/index.php/wos/article/view/6%0Ahttps://wos.academiascience.org/index.php/wos/article/download/6/6

A. Sabharwal, E. Stellrecht, and F. A. Scannapieco, “Associations between dental caries and systemic diseases: a scoping review,” BMC Oral Health, vol. 21, no. 1, pp. 1–35, 2021, doi: 10.1186/s12903-021-01803-w.

A. A. Ribeiro and B. J. Paster, “Dental caries and their microbiomes in children: what do we do now?,” J. Oral Microbiol., vol. 15, no. 1, 2023, doi: 10.1080/20002297.2023.2198433.

A. Imak, A. Celebi, K. Siddique, M. Turkoglu, A. Sengur, and I. Salam, “Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network,” IEEE Access, vol. 10, pp. 18320–18329, 2022, doi: 10.1109/ACCESS.2022.3150358.

R. N. Alotaibi et al., “Genome-Wide Association Study (GWAS) of dental caries in diverse populations,” BMC Oral Health, vol. 21, no. 1, pp. 1–11, 2021, doi: 10.1186/s12903-021-01670-5.

H. Alraqiq, A. Eddali, and R. Boufis, “Prevalence of dental caries and associated factors among school-aged children in Tripoli, Libya: a cross-sectional study,” BMC Oral Health, vol. 21, no. 1, pp. 1–12, 2021, doi: 10.1186/s12903-021-01545-9.

P. ForouzeshFar, A. A. Safaei, F. Ghaderi, and S. S. Hashemikamangar, “Dental Caries diagnosis from bitewing images using convolutional neural networks,” BMC Oral Health, vol. 24, no. 1, pp. 1–16, 2024, doi: 10.1186/s12903-024-03973-9.

T. Village, “Dental Caries Detection Through Resnet 50 Using Adam Optimizer,” vol. 7, no. 8, pp. 203–207, 2024.

J.-R. Lee, K.-W. Ng, and Y.-J. Yoong, “Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN,” J. Informatics Web Eng., vol. 2, no. 2, pp. 284–298, 2023, doi: 10.33093/jiwe.2023.2.2.20.

H. I. Sutomo, “Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture,” J. Appl. Intell. Syst., vol. 8, no. 3, pp. 320–330, 2023, doi: 10.33633/jais.v8i3.9064.

M. Jaiswal et al., “Deep Learning Models for Classification of Deciduous and Permanent Teeth From Digital Panoramic Images,” Cureus, vol. 15, no. 12, 2023, doi: 10.7759/cureus.49937.

S. Vinayahalingam et al., “Classification of caries in third molars on panoramic radiographs using deep learning,” Sci. Rep., vol. 11, no. 1, pp. 1–7, 2021, doi: 10.1038/s41598-021-92121-2.

Y. C. Mao et al., “Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs,” Sensors (Basel)., vol. 21, no. 13, 2021, doi: 10.3390/s21134613.

L. Lian, T. Zhu, F. Zhu, and H. Zhu, “Deep learning for caries detection and classification,” Diagnostics, vol. 11, no. 9, 2021, doi: 10.3390/DIAGNOSTICS11091672.

G. D. Deepak and S. Krishna Bhat, “Optimization of deep neural networks for multiclassification of dental X-rays using transfer learning,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 2023, doi: 10.1080/21681163.2023.2272976.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.

H. Mzoughi, I. Njeh, M. Ben Slima, and A. BenHamida, “Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging,” Multimed. Tools Appl., vol. 82, no. 25, pp. 39303–39325, 2023, doi: 10.1007/s11042-023-15097-3.

S. Gang, N. Fabrice, D. Chung, and J. Lee, “Circuit Board Using Deep Learning,” Sensors, 2021.

U. Seidaliyeva, D. Akhmetov, L. Ilipbayeva, and E. T. Matson, “Real-time and accurate drone detection in a video with a static background,” Sensors (Switzerland), vol. 20, no. 14, pp. 1–18, 2020, doi: 10.3390/s20143856.

S. Shivadekar, B. Kataria, S. Hundekari, K. Wanjale, V. P. Balpande, and R. Suryawanshi, “Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 1s, pp. 241–250, 2023.

L. Ali, F. Alnajjar, H. Al Jassmi, M. Gochoo, W. Khan, and M. A. Serhani, “Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures,” pp. 1–22, 2021.

F. Oztekin et al., “An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images,” Diagnostics, vol. 13, no. 2, 2023, doi: 10.3390/diagnostics13020226.

D. Salunke, D. Mane, R. Joshi, and P. Peddi, “Customized convolutional neural network to detect dental caries from radiovisiography(RVG) images,” Int. J. Adv. Technol. Eng. Explor., vol. 9, no. 91, pp. 827–838, 2022, doi: 10.19101/IJATEE.2021.874862.