计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 22-26.doi: 10.11896/jsjkx.210500197

• 智慧医疗 • 上一篇    下一篇

基于深度学习的黑色素瘤智能诊断多模型算法

常炳国, 石华龙, 常雨馨   

  1. 湖南大学信息科学与工程学院 长沙 410082
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 常炳国(changbingguo@126.com)
  • 基金资助:
    区块链底层技术及应用湖南省重点实验室;湖南省创新型省份建设专项(2020GK2006,2020GK2007)

Multi Model Algorithm for Intelligent Diagnosis of Melanoma Based on Deep Learning

CHANG Bing-guo, SHI Hua-long, CHANG Yu-xin   

  1. School of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:CHANG Bing-guo,born in 1963,Ph.D,researcher.His main research interests include computer application enginee-ring,information integration and artificial intelligence application research,etc.
  • Supported by:
    Hunan Key Laboratory of Blockchain Underlying Technology and Application and Special Project for the Construction of Innovative Provinces in Hunan Province(2020GK2006,2020GK2007).

摘要: 皮肤黑色素瘤是一种早期发现可治愈的疾病。目前诊断黑色素瘤的主要方法是基于皮肤镜的人工目视观察,较易受医师医技水平和经验的影响,诊断准确率为75%~80%,且诊断效率低。对此,文中提出一种融合元数据和图像数据的多模态神经网络算法。元数据是通过感知机学习模型提取的患者基本信息、病灶采集部位、图像分辨率和数量的特征向量;图像数据是通过CNN模型提取的特征向量,把两个特征向量进行融合映射以获得疾病分类结果,用于黑色素肿瘤的早期辅助诊断应用。收集整理了ISIC 2019和ISIC 2020的混合数据集,共58 457条样本数据,训练样本和测试样本按照4∶1比例进行划分,分别采用所提多模态算法和卷积神经网络方法进行对比实验研究,结果表明,使用所提算法构造的黑色素肿瘤辅助诊断分类模型能够将AUC值提升1%左右,证明其具有一定的使用价值。

关键词: 多模态算法, 辅助诊断, 黑色素瘤, 皮肤镜, 深度学习, 元数据

Abstract: Skin melanoma is a kind of disease that can be cured by early detection.The main diagnosis method is based on the manual visual observation of dermatoscope.Affected by the doctor's medical skill and experience,the diagnostic accuracy is 75%~80% and the diagnostic efficiency is low.In this paper,a multi-modal neural network algorithm based on metadata and image data is proposed.Metadata is the feature vector that extracts the basic information of patients,the location of lesions,the resolution and quantity of images through perceptual machine learning model.The image data is extracted from the feature vectors of CNN model,and the two feature vectors are fused and mapped to obtain the disease classification results,which can be used for early auxiliary diagnosis of melanoma.A total of 58 457 samples are collected from ISIC 2019 and ISIC 2020 mixed data sets.The training samples and test samples are divided according to the ratio of 4∶1.The multi-modal algorithm and convolutional neural network method proposed in this paper are used for comparative experimental research.The results show that the AUC value of the melanoma auxiliary diagnosis classification model constructed by this algorithm can be improved by about 1%,which has certain use value.

Key words: Auxiliary diagnosis, Deep learning, Dermoscopy, Melanoma, Metadata, Multimodal algorithm

中图分类号: 

  • TP242.6
[1] GAUTAM D,AHMED M,MEENA Y K,et al.Machine Learning Based Diagnosis of Melanoma Using Macro Images[C]//IEEE International Conference on Computer Vision.2017:211-223.
[2] SIEGEL R L,MILLER K D,JEMAl A.Cancer statistics,2020[J].CA:A Cancer Journal for Clinicians,2020,70(1).
[3] TAN M X,LE Q V.EfficientNet:Rethinking Model Scaling forConvolutional Neural Networks[J].arXiv:1905.11946,2019.
[4] JDINNES,DEEKS JONATHAN J,CHUCHU N,et al.Dermoscopy,with and without visual inspection,for the diagnosis of melanoma in adults[J].Cochrane Database of Systematic Reviews(Online),2018,3(11):101-121.
[5] DDHALL,KAUR R,JUNEJA M.Machine Learning:A Review of the Algorithms and Its Applications[C]//Proceedings of ICRIC 2019.2020.
[6] CHOWDHARY C L,ACHARJYA D P.Segmentation and Feature Extraction in Medical Imaging:A Systematic Review[C]//Procedia Computer Science.2020:16726-36.
[7] ZHANG H,WU C,ZHANG Z,et al.ResNeSt:Split-AttentionNetworks[J].arXiv:2004.08955,2020.
[8] DONG X,ZHIWEN Y U,CAO W,et al.A survey on ensemble learning[J].Frontiers of Computer Science,2020,14(2):241-258.
[9] WEISS C,KHOSHGOFTAAR T M,WANG D D.A survey of transfer learning[J].Journal of Big Data,2016,3(1):1-40.
[10] SHORTEN C,KHOSHGOFTAAR T M.A survey on ImageData Augmentation for Deep Learning[J].Journal of Big Data,2019,6(1).
[11] TOURASSI G D,ARMATO S G,RASTGOM,et al.Classification of Melanoma Lesions Using Sparse Coded Features and Random Forests[C]//Medical Imaging:Computer-aided Diagnosis.International Society for Optics and Photonics,2016:97850C.
[12] PREMALADHA J,RAVICHANDRAN K S.Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms[J].Journal of Medical Systems,2016,40(4):96.
[13] HOFFMANN K,GAMBICHLER T,RICKA,et al.Diagnosticand neural analysis of skin cancer(DANAOS).A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy[J].Br J Dermatol,2015,149(4):801-809.
[14] ROMERO-LOPEZ A,GIRO-I-NIETO X,BURDICK J,et al.Skin lesion classification from dermoscopic images using deep learning techniques[C]//Iasted International Conference on Biomedical Engineering.IEEE,2017.
[15] ZHEN Y,DONG N,CHEN S,et al.Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector[C]//IEEE International Symposium on Biomedical Imaging.IEEE,2017.
[16] MENEGOLA A,FORNACIALI M,PIRES R,et al.Knowledge Transfer for Melanoma Screening with Deep Learning[C]//IEEE International Symposium on Biomedical Imaging.2017:297-300.
[17] KAWAHARA J,HAMARNEH G.Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers[C]//Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging(MICCAI MLMI).Springer International Publishing,2016.
[18] NCODELLA N,NGUYEN Q B,PANKANTI S,et al.DeepLearning Ensembles for Melanoma Recognition in Dermoscopy Images[J].arXiv:1610.04662,2016.
[19] YU L,HAO C,QI D,et al.Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks[J].IEEE Transactions on Medical Imaging,2016,PP(99):994-1004.
[1] 饶志双, 贾真, 张凡, 李天瑞.
基于Key-Value关联记忆网络的知识图谱问答方法
Key-Value Relational Memory Networks for Question Answering over Knowledge Graph
计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[2] 汤凌韬, 王迪, 张鲁飞, 刘盛云.
基于安全多方计算和差分隐私的联邦学习方案
Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy
计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[3] 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺.
时序知识图谱表示学习
Temporal Knowledge Graph Representation Learning
计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[4] 王剑, 彭雨琦, 赵宇斐, 杨健.
基于深度学习的社交网络舆情信息抽取方法综述
Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[5] 郝志荣, 陈龙, 黄嘉成.
面向文本分类的类别区分式通用对抗攻击方法
Class Discriminative Universal Adversarial Attack for Text Classification
计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[6] 姜梦函, 李邵梅, 郑洪浩, 张建朋.
基于改进位置编码的谣言检测模型
Rumor Detection Model Based on Improved Position Embedding
计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046
[7] 孙奇, 吉根林, 张杰.
基于非局部注意力生成对抗网络的视频异常事件检测方法
Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection
计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[8] 胡艳羽, 赵龙, 董祥军.
一种用于癌症分类的两阶段深度特征选择提取算法
Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification
计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[9] 程成, 降爱莲.
基于多路径特征提取的实时语义分割方法
Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction
计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[10] 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木.
中文预训练模型研究进展
Advances in Chinese Pre-training Models
计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018
[11] 周慧, 施皓晨, 屠要峰, 黄圣君.
基于主动采样的深度鲁棒神经网络学习
Robust Deep Neural Network Learning Based on Active Sampling
计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044
[12] 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫.
小样本雷达辐射源识别的深度学习方法综述
Survey of Deep Learning for Radar Emitter Identification Based on Small Sample
计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138
[13] 刘伟业, 鲁慧民, 李玉鹏, 马宁.
指静脉识别技术研究综述
Survey on Finger Vein Recognition Research
计算机科学, 2022, 49(6A): 1-11. https://doi.org/10.11896/jsjkx.210400056
[14] 孙福权, 崔志清, 邹彭, 张琨.
基于多尺度特征的脑肿瘤分割算法
Brain Tumor Segmentation Algorithm Based on Multi-scale Features
计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217
[15] 康雁, 徐玉龙, 寇勇奇, 谢思宇, 杨学昆, 李浩.
基于Transformer和LSTM的药物相互作用预测
Drug-Drug Interaction Prediction Based on Transformer and LSTM
计算机科学, 2022, 49(6A): 17-21. https://doi.org/10.11896/jsjkx.210400150
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!