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REVIEW article

Front. Comput. Sci.
Sec. Computer Vision
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1423693

Lung Tumour Segmentation: A Review of the State-of-the-Art

Provisionally accepted
Anura Hiraman Anura Hiraman 1Serestina Viriri Serestina Viriri 1*Mandlenkosi Gwetu Mandlenkosi Gwetu 2
  • 1 University of KwaZulu-Natal, Durban, South Africa
  • 2 Stellenbosch University, Stellenbosch, Western Cape, South Africa

The final, formatted version of the article will be published soon.

    Lung cancer is the leading cause of cancer deaths worldwide. It is a type of cancer that commonly remains undetected due to unpresented symptoms until it has progressed to later stages which motivates the requirement for accurate methods of early detection of lung nodules. Computer-aided diagnosis systems have adapted to aid in detecting and segmenting lung cancer, which can increase a patient’s chance of survival. Automatic lung cancer detection and segmentation is a challenging task in aspects of segmentation accuracy. This paper provides a comprehensive review of current methods and popular techniques which will aid in further research in lung tumour detection and segmentation. This paper presents methods and techniques implemented to solve the challenges associated with lung cancer detection and segmentation and compares the approaches with each other. The methods used to evaluate these techniques and the accuracy rates are also discussed and compared to give insight for future research. Although several combination methods have been proposed over the past decade, an effective and efficient model still needs to be improvised for routine use.

    Keywords: detecting :::::: and ::::::::::::: segmenting :::::: lung ::::::::: cancer, which can increase a patient's chance of survival. Automatic lung cancer detection and segmentation is a challenging task in aspects of the accuracy of segmentation ::::::::::::::: segmentation :::::::::: accuracy. This paper serves to Lung Cancer, Lung Tumour Segmentation, deep learning, review, Survey Differences ::: in :::::::: imaging :::::::::: protocols :::::: such :: as :::::::: scanner ::::::::: models, :::::::: especially :: in :::::::::: low-dose :::::: scans :::::::::::::::::: (Song et al., ::::::::: Clustering

    Received: 26 Apr 2024; Accepted: 08 Oct 2024.

    Copyright: © 2024 Hiraman, Viriri and Gwetu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Serestina Viriri, University of KwaZulu-Natal, Durban, South Africa

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.