| 摘要: |
| 深度学习技术的迅猛发展为辅助医师进行高精度的疾病诊断提供了新的方法和思路。本文综述了医学疾病诊断领域常用的深度学习模型，即卷积神经网络、深度信念网络、受限玻尔兹曼机和循环神经网络模型的原理及特点；然后从肺癌、乳腺癌、糖尿病视网膜病变等几种典型的疾病出发，介绍了深度学习技术在疾病诊断领域的应用；最后基于目前深度学习技术在疾病诊断中的局限性提出了未来发展方向。 |
| 关键词: 人工智能 深度学习 疾病诊断 神经网络 |
| DOI：10.16781/j.0258-879x.2018.08.0852 |
| 投稿时间：2018-06-21修订日期：2018-08-01 |
| 基金项目: |
|
| Application of deep learning technology in disease diagnosis |
| WANG Wei,LI Yu,ZHANG Wen-juan,TIAN Ye,QIAN Ai-rong* |
(School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China
*Corresponding author) |
| Abstract: |
| The rapid development of deep learning technology provides new methods and ideas for achieving the goal of assisting doctors in high-precision diagnosis. In this paper, we summarized the principles and characteristics of deep learning models that are commonly used in disease diagnosis, including convolutional neural networks, deep belief network, restricted Boltzmann machine and circulation neural network model. Then we introduced the application of deep learning technology in disease diagnosis of several typical diseases, such as lung cancer, breast cancer, and diabetic retinopathy. Finally, we proposed the future of deep learning considering the limitations of deep learning technology in disease diagnosis. |
| Key words: artificial intelligence deep learning disease diagnosis neural networks |
