利用更快的基于区域的卷积神经网络和残差网络检测龋齿

Andre Citro Febriliyan Lanyak, Agi Prasetiadi, Haris Budi Widodo, Muhammad Hisyam Ghani, Abiyan Athallah
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引用次数: 0

摘要

到 2022 年,龋齿将成为全球发病率最高的牙科疾病。龋齿可以通过有效的筛查及早发现来阻止。此前,已有多种方法用于检测龋齿,如单枪多盒检测器(SSD)、基于区域的更快卷积神经网络(Faster R-CNN)和只看一次(YOLO)。本研究旨在利用 Faster R-CNN 开发精确的龋齿检测技术。本研究使用从互联网上搜索到的数据集,首先创建一个由 81 张基础图像组成的原始数据集,然后将其增加到总共 486 张图像,并由 Jenderal Soedirman 大学的牙科健康专家进行注释。使用预训练的 Faster R-CNN 残差网络 (ResNet)-50 和 ResNet-101 模型进行迁移学习,以检测和定位龋齿。使用 Adam 优化器训练的 Faster R-CNN ResNet-50 模型的平均精确度 (mAP) 为 0.213,而使用动量优化器训练的模型的平均精确度 (mAP) 为 0.177。而使用 Adam 优化器训练的 Faster R-CNN ResNet-101 模型的 mAP 为 0.192,使用动量优化器训练的模型的 mAP 为 0.004。在数据集上训练的模型在检测龋齿方面取得了令人满意的结果,尤其是使用 Adam 优化器的 ResNet-50。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dental caries detection using faster region-based convolutional neural network with residual network
Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are then augmented to a total of 486 images and annotated by dental health experts from Jenderal Soedirman University. Transfer learning using pre-trained Faster R-CNN residual network (ResNet)-50 and ResNet-101 model is utilized to detect and localise dental caries. The Faster R-CNN ResNet-50 model trained using the Adam optimizer produces a mean average precision (mAP) of 0.213, and those using the momentum optimizer produce a mAP of 0.177. While the Faster R-CNN ResNet-101 model trained using the Adam optimizer produces a mAP of 0.192, and those using the momentum optimizer produce a mAP of 0.004. The model trained on the dataset showed satisfactory results in detecting dental caries, especially ResNet-50 with Adam optimizer.
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