[1]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]
Copy
A skin diseases diagnosis method combining transfer learning and neural networks
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 3
Page number:
452-459
Column:
学术论文—自然语言处理与理解
Public date:
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
- CLC:
- TP391.1
- DOI:
- 10.11992/tis.201811015
- 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.
- References:
-
[1] 尤艳明, 李邻峰. 北京市海淀区社区人群常见皮肤病流行病学调查及风险因素分析[J]. 中国皮肤性病学杂志, 2011, 25(6): 459-461
YOU Y M, LI L F. The prevalence of skin diseases in a community of beijing and analysis of risk factors[J]. The Chinese journal of dermatovenereology, 2011, 25(6): 459-461
[2] JIANG Weiqin, SHEN Yifei, DING Yongfeng, et al. A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system[J]. International journal of cancer, 2018, 142(2): 357-368.
[3] SAYIN R, KESKIN S, HAMAMCI M. Evaluation of several classification methods in carpal tunnel syndrome[J]. The journal of the Pakistan medical association, 2017, 67(11): 1654-1657.
[4] EL BAKRAWY L M. Grey wolf optimization and naive Bayes classifier incorporation for heart disease diagnosis[J]. Australian journal of basic and applied sciences, 2017, 11(7): 64-70.
[5] LIU Jingzhou, CHANG Weicheng, WU Yuexin, et al. Deep learning for extreme multi-label text classification [C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Shinjuku, Japan, 2017: 115-124.
[6] 王诗琪, 刘洁, 朱晨雨. 皮肤科医师与深度卷积神经网络诊断色素痣和脂溢性角化病皮肤镜图像比较[J]. 中华皮肤科杂志, 2018, 51(7): 486-489
WAMG S Q, LIU J, ZHU C Y. Comparison of diagnostic performance of dermatologists versus deep convolutional neural network for dermoscopic images of pigmented nevus and seborrheic keratosis[J]. Chinese journal of dermatology, 2018, 51(7): 486-489
[7] ESTEVA A, KUPREL B, NOVOA R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118.
[8] 林伟铭, 高钦泉, 杜民. 卷积神经网络诊断阿尔兹海默症的方法[J]. 计算机应用, 2017, 37(12): 3504-3508
LIN Weiming, GAO Qinquan, DU Min. Convolutional neural network based method for diagnosis of Alzheimer’s disease[J]. Journal of computer applications, 2017, 37(12): 3504-3508
[9] DOU Qi, CHEN Hao, JIN Yueming, et al. Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning[C]//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, Canada, 2017: 630-638.
[10] WIMMER G, VéCSEI A, UHL A. CNN transfer learning for the automated diagnosis of celiac disease[C]//Proceedings of the 2016 Sixth International Conference on Image Processing Theory, Tools and Applications. Oulu, Finland, 2016: 1-6.
[11] CHENG Danni, LIU Manhua, FU Jianliang, et al. Classification of MR brain images by combination of multi-CNNs for AD diagnosis[C]//Proceedings of the SPIE 10420, Ninth International Conference on Digital Image Processing. Hong Kong, China, 2017: 1042042.
[12] CHE Zhengping, KALE D, LI Wenzhe, et al. Deep computational Phenotyping[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Australia, 2015: 507-516.
[13] DABEK F D, CABAN J J. A neural network based model for predicting psychological conditions[C]//Proceedings of 8th International Conference on Brain Informatics and Health. London, UK, 2015: 252-261.
[14] RUGHANI A I, DUMONT T M, LU Zhenyu, et al. Use of an artificial neural network to predict head injury outcome: clinical article[J]. Journal of neurosurgery, 2010, 113(3): 585-590.
[15] LASKO T A, DENNY J C, LEVY M A. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data[J]. PLoS one, 2013, 8(6): e66341.
[16] KIM Y. Convolutional neural networks for sentence classification[C]//In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 1746-1751.
[17] HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the national academy of sciences of the United States of America, 1982, 79(8): 2554-2558.
[18] LAI Siwei, XU Liheng, LIU Kang, et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin, USA, 2015: 2267-2273.
[19] 庄福振, 罗平, 何清, 等. 迁移学习研究进展[J]. 软件学报, 2015, 26(1): 26-39
ZHUANG Fuzhen, LUO Ping, HE Qing, et al. Survey on transfer learning research[J]. Journal of software, 2015, 26(1): 26-39
[20] PAN S J, YANG Q. A survey on transfer learning[J]. IEEE transactions on knowledge and data engineering, 2010, 22(10): 1345-1359.
[21] WEI Fengmei, ZHANG Jianpei, CHU Yan, et al. FSFP: transfer learning from long texts to the short[J]. Applied mathematics & information sciences, 2014, 8(4): 2033-2040.
- Similar References:
Memo
-
Last Update:
1900-01-01