Publication: The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
0
0
Issued Date
2022
Resource Type
Language
eng
File Type
application/pdf
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
Frontiers in Psychology. Vol 13, No. (2022), p.-
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. Frontiers in Psychology. Vol 13, No. (2022), p.-. doi:10.1109/ITC-CSCC55581.2022.9894916 Retrieved from: https://hdl.handle.net/20.500.14740/10013
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. © 2022 IEEE.