基于卷积神经网络和注意力机制的深度学习非小细胞肺癌计算机辅助诊断模型

刘治超; 许志文; 赵赛; 张又新; 聂聪; 雷子乔

doi:10.15953/j.ctta.2025.020

基于卷积神经网络和注意力机制的深度学习非小细胞肺癌计算机辅助诊断模型

刘治超^{1, 2, 3,},
许志文⁴,
赵赛⁵,
张又新^{1, 2, 3},
聂聪^{1, 2, 3},
雷子乔^{1, 2, 3, ,}

1.
华中科技大学同济医学院附属协和医院放射科，武汉 430022
2.
湖北省精准智能放射诊断与介入治疗临床医学研究中心，武汉 430022
3.
分子影像湖北省重点实验室，武汉 430022
4.
重庆大学大数据与软件工程学院，重庆 400044
5.
河北大学附属医院放射科，河北保定 071000

基金项目: 北京医学奖励基金会（YXJL-2024-0350-0096）。

详细信息

作者简介:
刘治超，男，硕士，技师，主要从事医学影像技术学、生物医学工程学等方面的研究，E-mail：lewisgetma@163.com

通讯作者:
雷子乔✉，男，医学博士，三级教授、主任技师、博士生导师、总技师长，哈佛大学医学院Brigham and Women’s Hospital高级研究学者，主要从事医学影像技术基础与临床应用、以及医学影像新技术与医工的结合，E-mail：ziqiao_lei@hust.edu.cn。

计量
- 文章访问数: 89
- HTML全文浏览量: 9
- PDF下载量: 19
出版历程
- 收稿日期: 2025-01-10
- 修回日期: 2025-02-26
- 录用日期: 2025-03-01
- 网络出版日期: 2025-03-22

Deep Learning Computer-aided Diagnostic Model for Non-small Cell Lung Cancer Based on Convolutional Neural Network and Attention Mechanism

LIU Zhichao^{1, 2, 3,},
XU Zhiwen⁴,
ZHAO Sai⁵,
ZHANG Youxin^{1, 2, 3},
NIE Cong^{1, 2, 3},
LEI Ziqiao^{1, 2, 3, ,}

1.
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
2.
Hubei Provincial Clinical Research Center for precision Radiology & Interventional Medicine, Wuhan 430022, China
3.
Hubei Province Key Laboratory of Moleculatr Imaging, Wuhan 430022, China
4.
School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China
5.
Department of Radiology, the Affiliated Hospital of Hebei University, Baoding 071000, China

摘要

摘要:
目的：构建基于卷积神经网络（CNN）和注意力机制的改进CNN模型（ISANET），评估模型的性能，并与传统CNN模型进行比较。方法：收集经手术病理证实的肺鳞癌（LUSC）或肺腺癌（LUAD）患者60例以及肺部正常患者30例的肺部CT平扫或增强图像共619张，组成DatasetA；收集公共数据集图像共737张，组成DatasetB。两数据集均按6∶4将随机分为训练集和测试集。构建ISANET模型并进行训练和验证，然后记录查准率、召回率，并计算出F1分数，用以评价ISANAT模型的效能。最后，将ISANET模型与传统CNN模型AlexNet，VGG 16，Inception V3，Mobilenet V2，ResNet 18进行对比，绘制P-R（P-R）曲线，计算出P-R曲线下面积，并评估不同模型对肺鳞癌和肺腺癌的鉴别效能。结果：相较于传统CNN模型，ISANET模型对非小细胞肺癌分类的准确度明显提高，在DatasetA和DatasetB中分别为99.6%和95.2%。结论：ISANET模型较好地实现了对肺鳞癌和肺腺癌的无创预测，提高了肺鳞癌和肺腺癌CT影像鉴别的准确度，能够帮助诊断医师对非小细胞肺癌进行快速准确的分类。
- 非小细胞肺癌 /
- 卷积神经网络 /
- 注意力机制 /
- 计算机辅助
Abstract:
Objective: To construct an improved deep learning computer-aided diagnosis model based on convolutional neural network (CNN) and Attention Mechanism proposed as Inception Spatial and Channel Attention Network (ISANET) and evaluate the model's performance, comparing it with the traditional CNN model. Methods: A total of 619 lung CT images of 60 patients with lung squamous cell carcinoma or lung adenocarcinoma confirmed by surgical pathology and 30 patients with normal lungs were collected retrospectively to form Dataset A; a total of 737 public dataset images were collected to form Dataset B. The two datasets were randomly divided into training and test sets at a 6:4 ratio. Construct the ISANET model and conduct training and verification, then record the precision ratio and recall ratio to calculate the F1 score and evaluate the performance of the ISANAT model. Finally, the ISANET model was compared with the traditional CNN models such as AlexNet, VGG16, InceptionV3, MobilenetV2, and ResNet18 by drawing the Precision-Recall (P-R) curve and calculating the area under the P-R curve to evaluate the classification performance of different models for LUSC and LUAD. Results: Compared with the traditional CNN model, the accuracy of the ISANET model for non-small cell lung cancer classification improved significantly, reaching 99.6% and 95.2% in Dataset A and Dataset B, respectively. Conclusions: The ISANET model provides better non-invasive prediction of LUSC and LUAD, improves the accuracy of CT imaging identification of lung squamous cell carcinoma and lung adenocarcinoma, and can help diagnosticians quickly and accurately classify non-small cell lung cancer.
- non-small cell lung cancer /
- CNN /
- attention mechanisms /
- computer-aided diagnosis

HTML全文

图 1 数据增强处理模式图

Figure 1. Data augmentation

下载: 全尺寸图片幻灯片

图 2 ISANET结构

Figure 2. The structure of ISANET

下载: 全尺寸图片幻灯片

图 3 通道注意力机制结构

Figure 3. The structure of CAM

下载: 全尺寸图片幻灯片

图 4 空间注意力机制结构

Figure 4. The structure of SA

下载: 全尺寸图片幻灯片

图 5 ISANET模型在不同数据集中的准确率和损失曲线

Figure 5. Accuracy and loss curves of ISANET on different datasets

下载: 全尺寸图片幻灯片

图 6 ISANET模型在不同数据集中的混淆矩阵

Figure 6. Confusion matrix of ISANET on different datasets

下载: 全尺寸图片幻灯片

图 7 各模型在不同数据集中的P-R曲线

Figure 7. P-R curves for each model in different datasets

下载: 全尺寸图片幻灯片

表 1 不同模型在DatasetA中的查准率、召回率和F1值比较情况

Table 1 Comparison of precision, sensitivity, and F1 score of different models in dataset A

算法名称	腺癌			鳞癌			未见明显异常
算法名称	查准率	召回率	F1值	查准率	召回率	F1值	查准率	召回率	F1值
ISANET	0.898	0.925	0.906	0.946	0.914	0.928	0.959	0.985	0.968
AlexNet	0.870	0.852	0.854	0.863	0.895	0.872	0.994	0.999	0.996
VGG16	0.798	0.789	0.761	0.764	0.857	0.771	0.995	0.992	0.994
InceptionV3	0.974	0.807	0.873	0.754	0.941	0.824	0.961	0.954	0.951
MobileNetV2	0.640	0.902	0.733	0.933	0.711	0.799	0.959	0.963	0.953
ResNet18	0.919	0.929	0.919	0.927	0.905	0.911	0.960	0.992	0.969

下载: 导出CSV

表 2 不同模型在DatasetB中的查准率、召回率和F1值比较情况

Table 2 Comparison of precision, sensitivity, and F1 score of different models in dataset B

算法名称	腺癌			鳞癌			未见明显异常
算法名称	查准率	召回率	F1值	查准率	召回率	F1值	查准率	召回率	F1值
ISANET	0.911	0.824	0.862	0.754	0.882	0.808	0.937	0.942	0.936
AlexNet	0.846	0.740	0.779	0.575	0.816	0.646	0.992	0.895	0.941
VGG16	0.814	0.720	0.717	0.507	0.668	0.531	0.964	0.949	0.952
InceptionV3	0.883	0.835	0.849	0.758	0.824	0.777	0.876	0.938	0.882
MobileNetV2	0.896	0.748	0.805	0.601	0.858	0.670	0.939	0.904	0.910
ResNet18	0.831	0.866	0.842	0.792	0.785	0.788	0.989	0.874	0.926

下载: 导出CSV

表 3 各模型在不同数据集中的准确度

Table 3 Accuracy of each model in different datasets

算法名称	Dataset A	Dataset B
ISANET	0.996	0.952
AlexNet	0.951	0.854
VGG16	0.939	0.901
InceptionV3	0.980	0.946
MobileNetV2	0.964	0.932
ResNet18	0.992	0.939

下载: 导出CSV

表 4 消融实验结果

Table 4 The results of ablation experiments

组别	Dataset A	Dataset B
A组	0.806	0.745
B组	0.810	0.735
C组	0.854	0.796
D组	0.802	0.721

下载: 导出CSV

参考文献(31)

[1]	HERBST R S, MORGENSZTERN D, BOSHOFF C. The biology and management of non-small cell lung cancer[J]. Nature, 2018, 553(7689): 446-454. DOI: 10.1038/nature25183.
[2]	中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2022版)[J]. 中华肿瘤杂志, 2022, 44(6): 457-490. DOI: 10.3760/cma.j.cn112152-20220413-00255. ONCOLOGY SOCIETY OF CHINESE MEDICAL ASSOCIATION, CHINESE MEDICAL ASSOCIATION PUBLISHING HOUSE. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2022 edition)[J]. Chinese Journal of Oncology, 2022, 44(6): 457-490. DOI: 10.3760/cma.j.cn112152-20220413-00255.
[3]	CARRILLO-PEREZ F, MORALES J C, CASTILLO-SECILLA D, et al. Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion[J]. BMC Bioinformatics, 2021, 22(1): 454. DOI: 10.1186/s12859-021-04376-1.
[4]	WOODARD G A, JONES K D, JABLONS D M. Lung cancer staging and prognosis[J]. Cancer Treatment and Research, 2016, 170: 47-75. DOI: 10.1007/978-3-319-40389-2_3.
[5]	CAO B, WANG P, GU L, et al. Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma[J]. Oncology Letters, 2021, 21(4): 249. DOI: 10.3892/ol.2021.12510.
[6]	KIM T-H, WOO S, YOON S H, et al. CT Characteristics of non–small cell lung cancer with anaplastic lymphoma kinase rearrangement: A systematic review and meta-analysis[J]. American Journal of Roentgenology, 2019, 213(5): 1059-1072. DOI: 10.2214/AJR.19.21485.
[7]	宋若晨, 褚相乐, 黄勇华, 等. 基于颅脑T1WI对比增强图像构建卷积神经网络模型鉴别肺癌与乳腺癌脑转移[J]. 中国医学影像技术, 2023, 39(7): 982-986. SONG R C, CHU X L, HUANG Y H, et al. Convolutional neural network model based on contrast-enhanced cranial T1WI for differentiating brain metastases from lung cancer or breast cancer[J]. Chinese Journal of Medical Imaging Technology, 2023, 39(7): 982-986.
[8]	LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42: 60-88. DOI: 10.1016/j.media.2017.07.005.
[9]	YAMASHITA R, NISHIO M, DO R K G, et al. Convolutional neural networks: An overview and application in radiology[J]. Insights into Imaging, 2018, 9(4): 611-629. DOI: 10.1007/s13244-018-0639-9.
[10]	RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252. DOI: 10.1007/s11263-015-0816-y.
[11]	SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 2818-2826.
[12]	COUDRAY N, OCAMPO P S, SAKELLAROPOULOS T, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning[J]. Nature Medicine, 2018, 24(10): 1559-1567. DOI: 10.1038/s41591-018-0177-5.
[13]	JIANG X, SHEN H. Classification of lung tissue with cystic fibrosis lung disease via deep convolutional neural networks[C]//Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine. Chengdu China: ACM, 2018: 113-116.
[14]	MOITRA D, KR. MANDAL R. Classification of non-small cell lung cancer using one-dimensional convolutional neural network[J]. Expert Systems with Applications, 2020, 159: 113564. DOI: 10.1016/j.eswa.2020.113564.
[15]	唐秉航, 王艳芳, 马力, 等. 基于混合注意力机制的肺结节假阳性降低[J]. CT理论与应用研究, 2022, 31(1): 63-72. DOI: 10.15953/j.ctta.2021.002. TANG B H, WANG Y F, MA L, et al. False positive reduction of pulmonary nodules based on mixed attentional raechanism[J]. CT Theory and Applications, 2022, 31(1): 63-72. DOI: 10.15953/j.ctta.2021.002.
[16]	TERAMOTO A, TSUKAMOTO T, KIRIYAMA Y, et al. Automated classification of lung cancer types from cytological images using deep convolutional neural networks[J]. BioMed Research International, 2017, 2017: 1-6.
[17]	TERAMOTO A, YAMADA A, KIRIYAMA Y, et al. Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network[J]. Informatics in Medicine Unlocked, 2019, 16: 100205. DOI: 10.1016/j.imu.2019.100205.
[18]	樊雪林, 文昱齐, 乔志伟. 基于Transformer增强型U-net的CT图像稀疏重建与伪影抑制[J]. CT理论与应用研究(中英文), 2024, 33(1): 1-12. DOI: 10.15953/j.ctta.2023.183. FAN X L, WEN Y Q, QIAO Z W. Sparse Reconstruction of Computed Tomography Images with Transformer Enhanced U-net[J]. CT Theory and Applications, 2024, 33(1): 1-12. DOI: 10.15953/j.ctta.2023.183.
[19]	KHAN M A, RAJINIKANTH V, SATAPATHY S C, et al. VGG19 network assisted joint segmentation and classification of lung nodules in CT images[J]. Diagnostics, 2021, 11(12): 2208. DOI: 10.3390/diagnostics11122208.
[20]	王媛媛, 周涛, 陆惠玲, 等. 基于集成卷积神经网络的肺部肿瘤计算机辅助诊断模型[J]. 生物医学工程学杂志, 2017, 34(4): 543-551. DOI: 10.7507/1001-5515.201607003. WANG Y Y, ZHOU T, LU H L, et al. Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network[J]. Journal of Biomedical Engineering, 2017, 34(4): 543-551. DOI: 10.7507/1001-5515.201607003.
[21]	AKSELROD-BALLIN A, KARLINSKY L, ALPERT S, et al. A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography[M]//Carneiro G, Mateus D, Peter L, et al. Deep Learning and Data Labeling for Medical Applications: Vol. 10008. Cham: Springer International Publishing, 2016: 197-205.
[22]	HELWAN A, EL-FAKHRI G, SASANI H, et al. Deep networks in identifying CT brain hemorrhage[J]. Journal of Intelligent & Fuzzy Systems, 2018, 35(2): 2215-2228.
[23]	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. DOI: 10.1038/nature21056.
[24]	HARANGI B. Skin lesion classification with ensembles of deep convolutional neural networks[J]. Journal of Biomedical Informatics, 2018, 86: 25-32. DOI: 10.1016/j.jbi.2018.08.006.
[25]	ALOM M Z, YAKOPCIC C, NASRIN MST S, et al. Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network[J]. Journal of Digital Imaging, 2019, 32(4): 605-617. DOI: 10.1007/s10278-019-00182-7.
[26]	SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: Inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 4510-4520.
[27]	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015: 1-9.
[28]	IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[M]. arXiv, 2015. http://arxiv.org/abs/1502.03167, 2015-03-02.
[29]	王坤, 王冠民, 路志凯. 动态增强CT对周围型肺癌的诊断价值[J]. 肿瘤研究与临床, 2021, 33(4): 304-306. DOI: 10.3760/cma.j.cn115355-20201127-00667.
[30]	薛彩强, 杜晓灏, 金龙, 等. 基于MRI深度学习模型预测WHOⅡ、Ⅲ级胶质瘤MGMT启动子甲基化状态[J]. 中华放射学杂志, 2021, 55(7): 734-738. XUE C K, DU X H, JIN L, et al. Prediction of methylation status of MGMT promoter in WHO grade Ⅱ, Ⅲ glioma based on MRI deep learning model[J]. Chinese Journal of Radiology, 2021, 55(7): 734-738.
[31]	孟宇, 马之骋, 阮敬儒, 等. 基于Faster R卷积神经网络构建胸部X线片异物智能检测模型的可行性研究[J]. 中华放射学杂志, 2022, 56(12): 1359-1364. MENG Y, MA Z C, RUAN J R, et al. Feasibility of constructing the intelligent detection model for foreign bodies on chest X-ray based on Faster R-convolutionalneural network[J]. Chinese Journal of Radiology, 2022, 56(12): 1359-1364.

施引文献(2)

期刊类型引用(2)

1.	刘蓉辉，何磊，叶玉芳，张淑倩，陈英敏. 新型冠状病毒感染的CT影像特点及演变规律. CT理论与应用研究. 2023(05): 695-701 . 本站查看
2.	王刚，谢浩锋，郑晓林，方学文，余芬芬，袁焕初，杜贺钦，邹玉坚. 新型冠状病毒肺炎临床与CT诊断特征的初步研究——东莞地区病例分析. CT理论与应用研究. 2020(04): 407-415 . 本站查看