About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Data and Information in Online Environments. Third EAI International Conference, DIONE 2022, Virtual Event, July 28-29, 2022, Proceedings

Research Article

Contributions and Limitations About the Use of Deep Learning for Skin Diagnosis: A Review

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-22324-2_11,
        author={Eduardo L. L. Nascimento and Angel Freddy Godoy Viera},
        title={Contributions and Limitations About the Use of Deep Learning for Skin Diagnosis: A Review},
        proceedings={Data and Information in Online Environments. Third EAI International Conference, DIONE 2022, Virtual Event, July 28-29, 2022, Proceedings},
        proceedings_a={DIONE},
        year={2022},
        month={12},
        keywords={Deep learning Skin lesion classification Skin lesion diagnostics Skin disease Dermatopathology Image recognition},
        doi={10.1007/978-3-031-22324-2_11}
    }
    
  • Eduardo L. L. Nascimento
    Angel Freddy Godoy Viera
    Year: 2022
    Contributions and Limitations About the Use of Deep Learning for Skin Diagnosis: A Review
    DIONE
    Springer
    DOI: 10.1007/978-3-031-22324-2_11
Eduardo L. L. Nascimento1,*, Angel Freddy Godoy Viera1
  • 1: Postgraduate Program in Information Science - Federal University of Santa Catarina, Florianópolis
*Contact email: nascimento.lln@gmail.com

Abstract

The aim of this study is to analyze the characteristics and applicability of Deep Learning (DL) models for the diagnosis of skin diseases. This study is characterized as a bibliographic review, exploratory-descriptive, qualitative in nature. Primary data was reported in the article databases. A total of 37 articles were analyzed to characterize the use of DL for the diagnosis of skin diseases. The survey results that public datasets access is mostly used in these surveys are (86%). The data collection that stood out was ISIC - International Skin Imaging Collaboration (54%). Greater commonly used data types in these models are images. Ultimately used model is the Convolutional Neural Network (CNN) and the uttermost used pre-trained model was ResNet. The most used techniques in the articles, in addition to classification (73%), focused on data segmentation (35%) and feature extraction (24%). The evaluation indicators that stand out are accuracy (89%), sensitivity (75%), and specificity (67%). The literature indicated that the approaches of studies that use DL for classification of skin diseases are very promising, however, practically all of the applied technologies have a greater need for interaction with clinical practices. As a suggestion for different works, studies that approach the task of DL work for diagnosis of different ethnic groups and their solutions for the democratization of such technologies.

Keywords
Deep learning Skin lesion classification Skin lesion diagnostics Skin disease Dermatopathology Image recognition
Published
2022-12-17
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-22324-2_11
Copyright © 2022–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL