基于多尺度融合卷积神经网络的熔解曲线有效性分类

doi:10.13229/j.cnki.jdxbgxb20200871

吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (3): 633-639.doi: 10.13229/j.cnki.jdxbgxb20200871

• 计算机科学与技术 • 上一篇

基于多尺度融合卷积神经网络的熔解曲线有效性分类

李向军^1,²(),涂洁莹¹,赵志宾¹()

^1.南昌大学软件学院，南昌 330047
^2.南昌大学计算机科学与技术系，南昌 330031

收稿日期:2020-11-11 出版日期:2022-03-01 发布日期:2022-03-08
通讯作者: 赵志宾 E-mail:lxjun_alex@163.com;zhaozhibin@ncu.edu.cn
作者简介:李向军（1972-），男，教授，硕士. 研究方向：人工智能及其应用.E-mail：lxjun_alex@163.com
基金资助:
国家自然科学基金项目(61672259);江西省科技创新平台项目(20181BCD40005);江西省主要学科学术和技术带头人项目(20172BCB22030);江西省自然科学基金项目(20192BAB207019);江西省研究生创新基金项目(YC2020-S028);江西省大学生实践创新训练项目(202110403068);江西省教改重点课题项目(JXJG-2020-1-2)

Validity classification of melting curve based on multi⁃scale fusion convolutional neural network

Xiang-jun LI^1,²(),Jie-ying TU¹,Zhi-bin ZHAO¹()

^1.College of Software，Nanchang University，Nanchang 330047，China
^2.Department of Computer Science and Technology，Nanchang University，Nanchang 330031，China

Received:2020-11-11 Online:2022-03-01 Published:2022-03-08
Contact: Zhi-bin ZHAO E-mail:lxjun_alex@163.com;zhaozhibin@ncu.edu.cn

摘要/Abstract

摘要：

针对熔解曲线图像中峰值分类的问题，提出了一种基于多尺度融合的卷积神经网络（CNN）分类模型。首先，将通过空洞卷积获取到的多尺度上下文信息与残差模块提取到的特征信息相融合，弥补了深层网络丢失全局信息的缺点。不同于传统卷积采用单一卷积核的方式，本文使用了一种卷积核的权值随输入而变化的动态滤波器，从而提高了网络学习的准确性。此外，为了提高模型的泛化能力，本文创建了熔解曲线的数据集，其中包括平衡数据集和不平衡数据集。将6个基于深度学习的分类方法与本文方法进行比较，结果表明本文方法在客观评价指标上明显优于其他方法。

关键词: 图像分类, 卷积神经网络, 多尺度融合, 熔解曲线, 动态滤波器

Abstract:

Aiming at the issue of peak classification in melting curve images， a convolutional neural network （CNN） classification model based on multi-scale fusion is proposed. First， the multi-scale context information obtained through dilated convolution is integrated with the feature information extracted from the residual module， which makes up for the shortcoming of the deep network that loses the global information. Then， different from traditional convolution with constant convolution kernel， a dynamic filter that changes with the input is used to make the network learning more accurate. In addition， in order to train the model， a dataset of melting curves is created， which includes a balanced dataset and an unbalanced dataset. This paper uses six classification-based evaluation indicators to compare with six classification methods based on deep learning. Experimental results show that the proposed method is significantly better than other methods in objective evaluation indicators.

Key words: image classification, convolutional neural networks, multi-scale fusion, melting curve, dynamic filter

中图分类号:

TP391

李向军,涂洁莹,赵志宾. 基于多尺度融合卷积神经网络的熔解曲线有效性分类[J]. 吉林大学学报(工学版), 2022, 52(3): 633-639.

Xiang-jun LI,Jie-ying TU,Zhi-bin ZHAO. Validity classification of melting curve based on multi⁃scale fusion convolutional neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 633-639.

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方法	Accuracy	Specificity	Sensitivity
ResNet-1	0.9231	0.8633	0.9425
ResNet-2	0.9253	0.8641	0.9434
ResNet-3	0.9272	0.8648	0.9421
ResNet-1_2	0.9292	0.8709	0.9503
ResNet-1_3	0.9301	0.8734	0.9527
ResNet-1_2_3	0.9325	0.8721	0.9532

方法	Accuracy	Specificity	Sensitivity
ResNet-18	0.9325	0.8721	0.9532
ResNet-34	0.9405	0.8793	0.9576
ResNet-50	0.9418	0.8837	0.9583
ResNet-101	0.9421	0.8843	0.9590
ResNet-152	0.9425	0.8848	0.9595

方法	Accuracy	Precision	Specificity	Sensitivity	F1	MCC
VGG	0.9071	0.9365	0.8464	0.9321	0.9343	0.7758
SqueezeNet	0.9142	0.9324	0.8459	0.9445	0.9384	0.7970
GoogLeNet	0.9179	0.9528	0.8776	0.9332	0.9429	0.7978
MnasNet	0.9075	0.9495	0.8720	0.9211	0.9350	0.7754
DenseNet	0.9101	0.9457	0.8619	0.9286	0.9371	0.7797
MobileNetV3	0.9120	0.9221	0.8371	0.9488	0.9352	0.7986
本文	0.9425	0.9623	0.8848	0.9595	0.9619	0.8454

基于多尺度融合卷积神经网络的熔解曲线有效性分类

Validity classification of melting curve based on multi⁃scale fusion convolutional neural network

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