计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (8): 1885-1897.DOI: 10.3778/j.issn.1673-9418.2011091
收稿日期:
2020-11-27
修回日期:
2021-01-29
出版日期:
2022-08-01
发布日期:
2021-03-03
通讯作者:
+E-mail: anfengping@163.com。作者简介:
安凤平(1985—),男,安徽芜湖人,博士,副教授,硕士生导师,主要研究方向为图像处理、深度学习。基金资助:
AN Fengping1,+(), LI Xiaowei1, CAO Xiang1
Received:
2020-11-27
Revised:
2021-01-29
Online:
2022-08-01
Published:
2021-03-03
About author:
AN Fengping, born in 1985, Ph.D., associate professor, M.S. supervisor. His research interests include image processing and deep learning.Supported by:
摘要:
深度学习在医学图像分类应用过程中存在以下问题:一是无法针对医学图像性质构建深度学习模型层级;二是深度学习模型网络初始化权重未能得到较好优化。为此,首先从网络优化角度出发,通过优化方法提高网络的非线性建模能力,提出了一种新的网络权重初始化方法,缓解了现有深度学习的初始化理论受限于非线性单元类型的问题,增加了神经网络处理不同视觉任务的潜力。同时,为了充分利用医学图像的特性,通过对多列卷积神经网络框架进行深入研究,发现通过改变卷积神经网络不同层次的特征数目和卷积核大小,可以构建不同的卷积神经网络模型,以更好地适应待处理医学图像的医学特性,并训练得到的异构多列卷积神经网络。最后,利用提出的一种自适应的滑动窗口融合机制,共同完成医学图像的分类任务。基于上述思想,提出了一种基于权重初始化-多层卷积神经网络滑动窗口融合的医学分类算法。利用提出方法对乳腺肿块分类、脑肿瘤组织分类实验和医学图像数据库分类分别进行实验,实验结果表明,所提方法不仅平均准确率较传统机器学习、其他深度学习方法有明显提高,而且具有较好的稳定性和鲁棒性。
中图分类号:
安凤平, 李晓薇, 曹翔. 权重初始化-滑动窗口CNN的医学图像分类[J]. 计算机科学与探索, 2022, 16(8): 1885-1897.
AN Fengping, LI Xiaowei, CAO Xiang. Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window CNN[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1885-1897.
Start | Range | Operation | 融合方法 |
---|---|---|---|
1 | 1 | — | Min rule |
N | 1 | — | Max rule |
— | N | Sum | Average rule |
— | N | Product | Product rule |
表1 滑动窗口融合方法的特例
Table 1 Special cases of sliding window fusion method
Start | Range | Operation | 融合方法 |
---|---|---|---|
1 | 1 | — | Min rule |
N | 1 | — | Max rule |
— | N | Sum | Average rule |
— | N | Product | Product rule |
分类方法 | 分类准确率/% |
---|---|
文献[48] | 95.4 |
CNN | 96.5 |
文献[49] | 96.7 |
文献[50] | 97.1 |
本文方法 | 98.2 |
表2 不同方法分类精度对比
Table 2 Comparison of classification accuracy of different methods
分类方法 | 分类准确率/% |
---|---|
文献[48] | 95.4 |
CNN | 96.5 |
文献[49] | 96.7 |
文献[50] | 97.1 |
本文方法 | 98.2 |
频次 | 训练样本 | 测试样本 |
---|---|---|
1 | 150 380 | 76 502 |
2 | 160 920 | 76 530 |
3 | 165 400 | 79 980 |
4 | 177 000 | 80 640 |
5 | 150 820 | 71 600 |
6 | 135 340 | 68 850 |
7 | 144 828 | 68 877 |
8 | 148 860 | 71 980 |
9 | 159 300 | 72 576 |
10 | 135 738 | 64 440 |
11 | 181 940 | 87 978 |
12 | 194 700 | 88 704 |
表3 每次实验样本情况
Table 3 Sample situation for each experiment
频次 | 训练样本 | 测试样本 |
---|---|---|
1 | 150 380 | 76 502 |
2 | 160 920 | 76 530 |
3 | 165 400 | 79 980 |
4 | 177 000 | 80 640 |
5 | 150 820 | 71 600 |
6 | 135 340 | 68 850 |
7 | 144 828 | 68 877 |
8 | 148 860 | 71 980 |
9 | 159 300 | 72 576 |
10 | 135 738 | 64 440 |
11 | 181 940 | 87 978 |
12 | 194 700 | 88 704 |
样本编号 | SVM | CNN | 文献[51] | 本文方法 |
---|---|---|---|---|
1 | 89.41 | 91.09 | 92.82 | 94.70 |
2 | 76.71 | 85.26 | 89.76 | 93.48 |
3 | 93.32 | 94.32 | 95.15 | 97.12 |
4 | 95.29 | 96.01 | 97.89 | 98.88 |
5 | 94.31 | 95.32 | 97.18 | 99.16 |
6 | 94.87 | 96.02 | 97.90 | 98.89 |
7 | 91.05 | 92.16 | 93.92 | 95.82 |
8 | 89.87 | 91.09 | 93.82 | 95.70 |
9 | 86.54 | 89.05 | 91.72 | 93.55 |
10 | 89.33 | 92.03 | 93.79 | 95.69 |
11 | 78.23 | 86.05 | 90.57 | 93.30 |
12 | 79.31 | 85.06 | 89.55 | 92.26 |
平均值 | 88.19 | 91.12 | 93.67 | 95.71 |
表4 脑肿瘤组织分类结果对比
Table 4 Comparison of classification results of
样本编号 | SVM | CNN | 文献[51] | 本文方法 |
---|---|---|---|---|
1 | 89.41 | 91.09 | 92.82 | 94.70 |
2 | 76.71 | 85.26 | 89.76 | 93.48 |
3 | 93.32 | 94.32 | 95.15 | 97.12 |
4 | 95.29 | 96.01 | 97.89 | 98.88 |
5 | 94.31 | 95.32 | 97.18 | 99.16 |
6 | 94.87 | 96.02 | 97.90 | 98.89 |
7 | 91.05 | 92.16 | 93.92 | 95.82 |
8 | 89.87 | 91.09 | 93.82 | 95.70 |
9 | 86.54 | 89.05 | 91.72 | 93.55 |
10 | 89.33 | 92.03 | 93.79 | 95.69 |
11 | 78.23 | 86.05 | 90.57 | 93.30 |
12 | 79.31 | 85.06 | 89.55 | 92.26 |
平均值 | 88.19 | 91.12 | 93.67 | 95.71 |
方法类型 | TCIA-CT | OASIS-MRI |
---|---|---|
LBP+SVM | 71.8 | 57.5 |
HOG+KNN | 85.1 | 67.6 |
HOG+SVM | 87.3 | 81.6 |
DeepNet1 | 98.7 | 89.2 |
DeepNet3 | 99.2 | 92.1 |
本文方法 | 100.0 | 95.9 |
表5 不同算法在医学图像数据库的分类精度
Table 5 Classification accuracy of different algorithms in medical image database %
方法类型 | TCIA-CT | OASIS-MRI |
---|---|---|
LBP+SVM | 71.8 | 57.5 |
HOG+KNN | 85.1 | 67.6 |
HOG+SVM | 87.3 | 81.6 |
DeepNet1 | 98.7 | 89.2 |
DeepNet3 | 99.2 | 92.1 |
本文方法 | 100.0 | 95.9 |
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