神经网络龋病检测的综合探索

V. G, Kandukuru Swaroop Krishna, Mallempati Uday Kiran, Shaik Nihal, K. V, U. Ramachandraiah
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引用次数: 0

摘要

龋齿是一种由细菌引起的疾病,随着时间的推移会恶化,是导致牙齿脱落的最常见原因。这是缺乏口腔卫生的结果,它还会导致各种牙齿疾病。如果可以通过远程牙科技术在早期发现龋齿,儿童的牙齿健康将大大受益。因为严重的龋齿会引起疾病和不适,所以可能需要拔牙。因此,早期发现和诊断这些龋齿是研究人员的首要任务。软计算技术通常用于牙科,以简化诊断和减少筛查时间。本研究的目的是利用x射线扫描图像及早发现蛀牙,以便及时有效地完成治疗。作为一种远程信息口腔保健系统,这种分类也适用于远程牙科保健。我们在建议的工作中使用了卷积神经网络(CNN)深度学习模型。我们训练了几个CNN深度学习模型。在有和没有龋齿照片的二值数据集上进行训练和测试。注意到CNN模型的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Exploration of Neural Networks for Dental Caries Detection
Dental caries, an illness due to bacteria that worsens with time, is the most common cause of tooth loss. This occurs as an outcome of least oral hygiene, which in addition contributes to a variety of dental disorders. Children's dental health will benefit considerably if caries can be detected at an early stage via tele-dentistry technology. Because severe caries causes disease and discomfort, tooth extraction may be necessary. As a result, early detection and diagnosis of these caries are the researchers' priority priorities. Soft computing techniques are commonly employed in dentistry to simplify diagnosis and reduce screening time. The goal of this study is to employ x-ray scanned images to detect dental cavities early on so that treatment can be completed promptly and effectively. As a tele-informatic oral health care system, this classification also applies to tele-dental care. We used a convolution neural network (CNN) deep learning model in the suggested work. We trained several CNN deep learning models. Training and testing were performed on a binary dataset with and without caries photos. The classification precision of CNN models is noted.
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