The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
1
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85140629282
Journal Title
ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications
Start Page
235
End Page
238
Rights Holder(s)
SCOPUS
Bibliographic Citation
ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications (2022) , 235-238
Suggested Citation
Phosri K., Treebupachatsakul T., Chomkwah W., Tanpatanan T., Thanathornwong B., Khovidhunkit S.O.P., Poomrittigul S. The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions. ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications (2022) , 235-238. 238. doi:10.1109/ITC-CSCC55581.2022.9894916 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84623
Title
The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
Other Contributor(s)
Abstract
Oral cancer is one of the top health problems globally. Some white lesions of the oral cavity can develop into oral cancer if not screened and treated immediately. Modern screening technologies are popular for applying deep learning knowledge to screen and classify images. In this study, we used deep convolution neural network (CNN) to classify oral white lesions, ulcers, and normal anatomy using transfer learning, which can reduce training time. Ten pre-trained model of transfer learning including DenseNet121, DenseNet169, DenseNet201, Xception, ResNet50, InceptionResNetV2, InceptionV3, VGG16, VGG19, and EfficientNetB7 are implemented and evaluated. The evaluation of accuracy, precision, F1score, recall, sensitivity, confusion matrix, and AUC-ROC curve are discussed. The trained models of DenseNet169, DenseNet201, and Xception showed the highest testing accuracy of more than 90% and recall of 0.8833. In addition to the precision, F1score, and specificity, the DenseNet169 outperforms at 0.9034, 0.884, and 0.9417, respectively.