Development of Radiation Dermatitis Grading Method based on Bayesian Estimation using EfficientNet

Abstract

Radiation dermatitis is classified into 5 grades from 1, representing mild disease to 5, death due to adverse events based on the Common Terminology Criteria for Adverse Events used in clinical practice. However, it is based on visual assessment; therefore, there are issues that depend on individual experience and knowledge. We have generated artificial case images and constructed a hybrid generation method for a radiation dermatitis grading support system using deep learning to deal with the limitation posed by the relatively small number of cases used for learning. In this paper, we created a model using the recently proposed EfficientNet model. Additionally, we described the method of final classification based on the Bayesian estimation of images, which are graded differently by evaluators. The learning model using EfficientNet-B0 to B7 with different image resolution and data extension method conditions showed an overall accuracy of 86.4%. Moreover, the final grade judgment was performed with multiple EfficientNet models to resolve the ambiguity of grade judgment. The effectiveness of the proposed method was confirmed via an evaluation experiment using the maximum a posteriori estimation method based on the Bayesian’ theorem （Bayesian estimation）.