[1]方谦昊,朱 红,何瀚志,等.基于卷积神经网络的脑膜瘤亚型影像自动分级[J].南京师范大学学报(自然科学版),2018,41(03):22.[doi:10.3969/j.issn.1001-4616.2018.03.004]
　Fang Qianhao,Zhu Hong,He Hanzhi,et al.Automatic Classification of Meningioma Subtype ImageBased on Convolutional Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(03):22.[doi:10.3969/j.issn.1001-4616.2018.03.004]
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基于卷积神经网络的脑膜瘤亚型影像自动分级()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第41卷
期数:
2018年03期
页码:
22
栏目:
·人工智能算法与应用专栏·
出版日期:
2018-09-30

文章信息/Info

Title:
Automatic Classification of Meningioma Subtype ImageBased on Convolutional Neural Network
文章编号:
1001-4616(2018)03-0022-06
作者:
方谦昊; 朱 红; 何瀚志; 胡俊峰
徐州医科大学医学信息学院,江苏 徐州 221004
Author(s):
Fang Qianhao; Zhu Hong; He Hanzhi; Hu Junfeng
School of Medical Information,Xuzhou Medical University,Xuzhou 221004,China
关键词:
脑膜瘤; 卷积神经网络; LeNet-5; 脑膜瘤亚型影像分级
Keywords:
meningioma; convolutional neural network; LeNet-5; meningioma subtype image classification
分类号:
TP181
DOI:
10.3969/j.issn.1001-4616.2018.03.004
文献标志码:
A
摘要:
脑膜瘤是颅内常见第二大肿瘤. 脑膜瘤的术前分级有助于临床制定治疗方案和评估预后. 本文对卷积神经网络LeNet-5模型从softmax层、网络结构、迭代下降速率、epoch几个方面进行改进,用于对脑膜瘤亚型影像的自动分级. 该模型不需要对病变组织进行提取,大大提高了脑膜瘤影像自动分级效率. 实验表明:改进的卷积神经网络模型对脑膜瘤亚型影像分级取得良好效果,最高正确率达到91.18%.
Abstract:
Meningioma is the second most common tumor in the brain. The preoperative grading of meningioma can help develop clinical treatment plans and evaluate prognosis. In this paper,the LeNet-5 model of the convolutional neural network is improved from several aspects which include softmax layer,network structure,iterative rate of decline,and epoch for automatic classification of subtype images of meningioma. The model does not require the extraction of diseased tissue,which greatly improves the efficiency of automatic classification of meningioma images. Experiments show that the improved convolutional neural network model has a good effect on the classification of meningioma subtype images,and the highest correct rate reaches 91.18%.

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备注/Memo

备注/Memo:
收稿日期:2018-04-16.
基金项目:江苏省自然科学基金(BK20130209)、江苏省高校自然科学基金(14KJB520039).
通讯联系人:朱红,教授,研究方向:人工智能、医学图像处理、机器学习与粒度计算. E-mail:zhuhongwin@126.com
更新日期/Last Update: 2018-11-19