基于卷积神经网络的牙周病检测

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI:10.1109/ICAIIC.2019.8669021

Jaehan Joo, Sinjin Jeong, Heetae Jin, Uhyeon Lee, Ji Young Yoon, S. Kim

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引用次数: 12

摘要

本文提出了一种基于CNN的牙周病分类方法。使用的数据是实际牙周图像和非牙周图像。采用调整大小、裁剪和零集中化等数据处理技术提高数据学习效率。本文提出的CNN结构以224 × 224 × 3大小的图像作为输入数据，并根据牙周状态输出4个输出。我们还将动量优化技术用于神经网络的优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Periodontal Disease Detection Using Convolutional Neural Networks

In this paper, we propose a classification method of periodontal disease based on CNN. The data to used were the actual periodontal images and non-periodontal images. Data processing techniques such as resize, crop and zero-centralizing are used to improve data learning efficiency. The CNN Structure proposed in this paper has 224 × 224 × 3 size image as input data and 4 outputs according to periodontal state. We also use momentum optimization technique for neural network optimization.

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2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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