Exploring Deep Learning Techniques in Skin Lesion Detection: A Survey

  • Vidhu Vinod*
  • , Sameena Pathan
  • , Anetha Mary Soman
  • *Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Skin cancer presents a significant global health concern, with its incidence steadily increasing over recent decades. This disease affects millions worldwide, presenting considerable challenges to healthcare systems and societies. Skin lesions serve as crucial markers for diagnosing and monitoring skin cancer, revealing abnormal cell growth visibly. Given the escalating prevalence of skin cancer and the imperative for early detection, there is a pressing need for computer-aided tools in skin lesion detection. Researchers have utilized artificial intelligence (AI) and deep learning to advance lesion detection, addressing this urgent requirement. Amid the rising prevalence of skin cancer, this paper delves into various deep learning methodologies, including convolutional neural networks (CNNs) and computer vision techniques, which have shown promising outcomes in automating skin lesion detection comparable to expert dermatologists.

    Original languageEnglish
    Title of host publicationProceedings of Data Analytics and Management, ICDAM 2024
    EditorsAbhishek Swaroop, Bal Virdee, Sérgio Duarte Correia, Zdzislaw Polkowski
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages529-537
    Number of pages9
    ISBN (Print)9789819633548
    DOIs
    Publication statusPublished - 2025
    Event5th International Conference on Data Analytics and Management, ICDAM 2024 - London, United Kingdom
    Duration: 14-06-202415-06-2024

    Publication series

    NameLecture Notes in Networks and Systems
    Volume1298 LNNS
    ISSN (Print)2367-3370
    ISSN (Electronic)2367-3389

    Conference

    Conference5th International Conference on Data Analytics and Management, ICDAM 2024
    Country/TerritoryUnited Kingdom
    CityLondon
    Period14-06-2415-06-24

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Signal Processing
    • Computer Networks and Communications

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