Deep learning empowered decision support systems for thyroid cancer detection and management

Xinyu Zhang, Vincent C.S. Lee

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

Abstract

Exploiting the capability of deep learning-based techniques, this research addresses an important and relevant problem on how to cost effectively detect thyroid cancer. In recent decades, there has been a significant increase in the incidence of thyroid cancer, prompting the need to understand its epidemiology. Existing studies have primarily employed qualitative techniques to investigate a single risk factor correlated with the disease development at a time. However, such an approach is inefficient and tends to ignore the interwoven connections among factors, resulting in a considerable disagreement with the identified risk factors among scholars. Additionally, the use of deep learning techniques in conjunction with medical imaging for computer-aided diagnostic (CAD) systems design has shown promises in detecting the disease, while there are research gaps regarding the detection of subtype and their co-existence. More importantly, existing CAD systems have shortcomings in adapting to different sample groups. To address these challenges, this research aims to shed light on the pathogenesis of thyroid cancer, enhance diagnostic performance, and improve generalisation of deep learning-based decision support systems. By harnessing the power of machine learning, specifically data mining and deep learning techniques, we seek to improve our understanding of the underlying mechanisms of the disease and develop innovative, robust, accurate, and efficient diagnostic tools. Extensive experiments indicate superior performance of the proposed methods than existing works. The systems proposed in this study have great impact to the wider society and contribute to the advancement of thyroid cancer research while enhancing clinical practice in related detection and subsequent management.

Original languageEnglish
Title of host publicationInternational Conference on Industry Sciences and Computer Science Innovation 2023
EditorsMaria Manuela C. C. Manuela Cruz Cunha, Nuno N Mateus-Coelho
Place of PublicationPortugal
PublisherElsevier
Pages945-954
Number of pages10
Volume237
DOIs
Publication statusPublished - 2024
EventInternational Conference on Industry Sciences and Computer Science Innovation 2023 - Lisbon, Portugal
Duration: 4 Oct 20236 Oct 2023
https://www.sciencedirect.com/journal/procedia-computer-science/vol/237/suppl/C (Proceedings)
https://iscsi-conference.org (Website)

Publication series

NameProcedia Computer Science
PublisherElsevier
ISSN (Print)1877-0509

Conference

ConferenceInternational Conference on Industry Sciences and Computer Science Innovation 2023
Abbreviated titleiSCSi 2023
Country/TerritoryPortugal
CityLisbon
Period4/10/236/10/23
Internet address

Keywords

  • CAD
  • convolutional neural network
  • data mining
  • Deep learning
  • disease pathogenesis
  • thyroid cancer

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