Deep learning for image-based cancer detection and diagnosis - A survey

被引:286
|
作者
Hu, Zilong [1 ]
Tang, Jinshan [1 ,2 ,3 ]
Wang, Ziming [2 ]
Zhang, Kai [1 ,3 ]
Zhang, Ling [1 ]
Sun, Qingling [4 ]
机构
[1] Michigan Technol Univ, Sch Technol, Houghton, MI 49931 USA
[2] Michigan Technol Univ, Dept Elect & Comp Engn, Houghton, MI 49931 USA
[3] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430065, Hubei, Peoples R China
[4] Sun Technol & Serv LLC, Clinton, MS 39056 USA
基金
美国国家卫生研究院;
关键词
BRAIN-TUMOR SEGMENTATION; CONVOLUTIONAL NEURAL-NETWORK; COMPUTER-AIDED DIAGNOSIS; FALSE-POSITIVE REDUCTION; LUNG NODULE; MITOSIS DETECTION; CLASSIFICATION; ALGORITHMS; DATABASE; MASSES;
D O I
10.1016/j.patcog.2018.05.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we aim to provide a survey on the applications of deep learning for cancer detection and diagnosis and hope to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto-encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for cancer detection and diagnosis. The surveys in this part are organized based on the types of cancers. Thirdly, we provide a summary and comments on the recent work on the applications of deep learning to cancer detection and diagnosis and propose some future research directions. (C) 2018 Published by Elsevier Ltd.
引用
收藏
页码:134 / 149
页数:16
相关论文
共 50 条
  • [31] A survey on deep learning-based image forgery detection
    Mehrjardi, Fatemeh Zare
    Latif, Ali Mohammad
    Zarchi, Mohsen Sardari
    Sheikhpour, Razieh
    [J]. PATTERN RECOGNITION, 2023, 144
  • [32] Deep Learning in Image-Based Plant Phenotyping
    Murphy, Katherine M.
    Ludwig, Ella
    Gutierrez, Jorge
    Gehan, Malia A.
    [J]. ANNUAL REVIEW OF PLANT BIOLOGY, 2024, 75 : 771 - 795
  • [33] Image-based cell phenotyping with deep learning
    Pratapa, Aditya
    Doron, Michael
    Caicedo, Juan C.
    [J]. CURRENT OPINION IN CHEMICAL BIOLOGY, 2021, 65 : 9 - 17
  • [34] Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment
    Ullah, Irfan
    Khan, Rehan Ullah
    Yang, Fan
    Wuttisittikulkij, Lunchakorn
    [J]. ENERGIES, 2020, 13 (02)
  • [35] Deep Learning Approaches for Image-Based Detection and Classification of Structural Defects in Bridges
    Cardellicchio, Angelo
    Ruggieri, Sergio
    Nettis, Andrea
    Patruno, Cosimo
    Uva, Giuseppina
    Reno, Vito
    [J]. IMAGE ANALYSIS AND PROCESSING, ICIAP 2022 WORKSHOPS, PT I, 2022, 13373 : 269 - 279
  • [36] Deep Learning Model for Image-Based Plant Diseases Detection on Edge Devices
    Chaitra, S.
    Ghana, Satyajit
    Singh, Shikhar
    Poddar, Prachi
    [J]. 2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,
  • [37] Rapid and sensitive mycoplasma detection system using image-based deep learning
    Iseoka, Hiroko
    Sasai, Masao
    Miyagawa, Shigeru
    Takekita, Kazuhiro
    Date, Satoshi
    Ayame, Hirohito
    Nishida, Azusa
    Sanami, Sho
    Hayakawa, Takao
    Sawa, Yoshiki
    [J]. JOURNAL OF ARTIFICIAL ORGANS, 2022, 25 (01) : 50 - 58
  • [38] Automatic Seizure Detection via an Optimized Image-based Deep Feature Learning
    Alkanhal, Ibrahim
    Kumar, B. V. K. Vijaya
    Savvides, Marios
    [J]. 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, : 536 - 540
  • [39] Systematic Study of Deep Learning Models for Image-Based Detection of Monkeypox Virus
    Llamas, Vanessa Melenciano
    de la Rosa Trejo, Miguel Angel
    Arroyo Castorena, Hugo Geovani
    Ibarra Belmonte, Isaul
    [J]. 2023 12TH INTERNATIONAL CONFERENCE ON SOFTWARE PROCESS IMPROVEMENT, CIMPS 2023, 2023, : 225 - 233
  • [40] Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning
    Das, Amrita
    Dorafshan, Sattar
    Kaabouch, Naima
    [J]. SENSORS, 2024, 24 (11)