Comparison of Efficacy of Mask-RCNN Models with Different Layers in Automatic Detection of Ameloblastoma

doi:10.13701/j.cnki.kqyxyj.2023.12.012

Abstract

Abstract: Objective: To compare the performance of different layers of ResNet models in CT image detection of Ameloblastoma(AME), which uses artificial intelligence deep learning with Mask-RCNN. Methods: A retrospective analysis was carried out on CT image data of AME patients at the First Affiliated Hospital of Fujian Medical University from April 2018 to August 2020. In the study, 79 patients were included based on specific criteria. After preprocessing, we obtained 3566 images, which were divided into training, validation, and test sets in an 8∶1∶1 ratio randomly. After ResNet-18, ResNet-50, and ResNet-101 models were employed for training, automatic tumor detection was achieved, and metrics such as Dice coefficient, average accuracy, and detection time were evaluated. Results: Compared to ResNet-18 and ResNet-50, the ResNet-101 model exhibited the best performance in automatic detection, with a Dice coefficient of 0.87 and an average accuracy AP(IOU 0.50∶0.95) of 0.74. However, this model also had the longest detection time, approximately 0.33 seconds. Conclusion: The Mask-RCNN model with different layers can realize the automatic detection of AME.Among the models studied, ResNet-101 demonstrated the most favorable detection performance, though a longer detection time.

Key words: ameloblastoma, deep learning, Mask-RCNN, ResNet

LAI Danlin, XU Liang, NI Jianzhao, ZHU Xiaofeng, HUANG Xiaohong. Comparison of Efficacy of Mask-RCNN Models with Different Layers in Automatic Detection of Ameloblastoma[J]. Journal of Oral Science Research, 2023, 39(12): 1092-1096.

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