[1]商显震,韩萌,王少峰,等.融合迁移学习和神经网络的皮肤病诊断方法[J].智能系统学报,2020,15(3):452-459.[doi:10.11992/tis.201811015]
SHANG Xianzhen,HAN Meng,WANG Shaofeng,et al.A skin diseases diagnosis method combining transfer learning and neural networks[J].CAAI Transactions on Intelligent Systems,2020,15(3):452-459.[doi:10.11992/tis.201811015]
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SHANG Xianzhen,HAN Meng,WANG Shaofeng,et al.A skin diseases diagnosis method combining transfer learning and neural networks[J].CAAI Transactions on Intelligent Systems,2020,15(3):452-459.[doi:10.11992/tis.201811015]
融合迁移学习和神经网络的皮肤病诊断方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第3期
页码:
452-459
栏目:
学术论文—自然语言处理与理解
出版日期:
2020-05-05
- Title:
- A skin diseases diagnosis method combining transfer learning and neural networks
- Keywords:
- skin disease diagnosis; neural network; transfer learning; text classification; convolutional neural network; recurrent neural network; long short term memory neural network; auxiliary diagnosis
- 分类号:
- TP391.1
- 摘要:
- 针对医学特征对患者病情发展的时间顺序无法有效表达，医学特征构建工作耗费大量人工成本，以及皮肤病数据样本数量较少等问题，提出了融合迁移学习和神经网络的皮肤病辅助诊断方法。该方法将TextLSTM(long short term memory neural network for text)、TextCNN(convolutional neural network for text)以及RCNN(recurrent convolutional neural networks for text classification)等3种基于神经网络的文本分类模型应用于皮肤病辅助诊断，同时融入迁移学习技术，能够在一定程度上将皮肤病专业书籍中的理论知识迁移到诊断模型中。在皮肤病多分类实验中，本文方法的正确率优于对比方法；在皮肤病二分类实验中，本文方法的召回率优于对比方法。迁移学习对实验结果的积极影响率高于75%。
- Abstract:
- To address the problems that medical features can not effectively express the chronological order of a patient’s condition, feature construction incurs high labor costs, and the number of diagnosed cases of skin diseases is relatively low, this study proposes binary classification and multi-classification diagnostic methods based on neural network and transfer learning of multisource data for diagnosing skin diseases. The text classification model based on three neural network models, namely, TextLSTM(long short term memory neural network for text), TextCNN(convolutional neural network for text), and RCNN(recurrent convolutional neural networks for text classification), is applied to dermatological auxiliary diagnosis. At the same time, the method incorporates transfer learning, which can transfer theoretical knowledge of skin diseases obtained from books to the diagnostic models to a certain degree. Results show that the accuracy rate of the multi-classification diagnostic method is higher than that of the binary classification diagnostic method. By contrast, the recall rate of the binary classification diagnostic method is higher than that of the multi-classification diagnostic method. Thus, transfer learning has a positive effect on more than 75% of the experimental results.
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备注/Memo
收稿日期:2018-11-21。
基金项目:国家自然科学基金项目(61563001)；宁夏自然科学基金项目(NZ17115)；计算机应用技术宁夏回族自治区重点学科项目(PY1703)
作者简介:商显震，硕士研究生，主要研究方向为数据挖掘、机器学习;韩萌，副教授，博士，主要研究方向为数据挖掘、机器学习。主持国家自然科学基金、宁夏自然科学基金等多个基金项目。发表学术论文30余篇;王少峰，硕士研究生，主要研究方向为数据挖掘
通讯作者:韩萌.E-mail:2003051@nun.edu.cn
更新日期/Last Update:
1900-01-01