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The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
Author(s)
Phosri, Kunchidsong
Chomkwah, Wanwalee
Tanpatanan, Tananan
Thanathornwong, Bhornsawan
Khovidhunkit, Siribang On Piboonniyom
Date Issued
January 1, 2022
Type
Conference Paper
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.
Citation
Itc Cscc 2022 37th International Technical Conference on Circuits Systems Computers and Communications, 235-238, 2022