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A comprehensive review of extreme learning machine on medical imaging

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datacite.rightshttp://purl.org/coar/access_right/c_abf2
dc.contributor.authorHuérfano Maldonado, Yoleidy
dc.contributor.authorMora, Marco
dc.contributor.authorVilches, Karina
dc.contributor.authorHernández García, Ruber
dc.contributor.authorGutiérrez, Rodrigo
dc.contributor.authorVera, Miguel
dc.date.accessioned2025-01-17T20:06:01Z
dc.date.available2025-01-17T20:06:01Z
dc.date.issued2023
dc.description.abstractThe feedforward neural network based on randomization has been of great interest in the scientific community, particularly extreme learning machines, due to its simplicity, training speed, and levels of accuracy comparable to traditional learning algorithms. Extreme learning machines (ELMs) are a type of artificial neural network (ANN) with one or more hidden layers that are trained under supervised, unsupervised, or semi-supervised learning approaches. These networks are widely used in various research areas, such as medical image processing (MI). This research work presents an exhaustive review of extreme learning machines (ELM) and medical image processing (MI), due to the high impact that these networks have had on the scientific community and the importance of MI for physicians who use them to diagnose different injuries and diseases. First, the theoretical construct of ELMs is developed based on the types of supervised, unsupervised, and semi-supervised learning. Then, the importance of MI for the diagnosis of a disease or classification of the most commonly used imaging modalities is analyzed for articles concerning radiography, computed tomography (CT), magnetic resonance (MR), ultrasound (US), and mammography (MG). Next, the reference data sets linked to various human body organs, such as the brain, lungs, skin, eyes, breasts, and cervix are described. Then, a review, analysis, and classification of the development of the last 6 years (2017–2022) of ELMs, based on learning types and MI, is performed. With the information obtained above, a construction of summary tables of the articles, classified according to the type of learning, is performed, highlighting the organ, reference, year, methodology, database, modality, and results. Finally, the discussion, conclusions and challenges related to this topic are presented. The findings indicate that the review articles reported in the literature have not addressed the relationship between ELMs and medical imaging in depth and have excluded key aspects, which are developed in this article. These aspects include a comprehensive analysis of the most popular imaging modalities, a detailed description of both the most popular databases and the most relevant databases for the machine learning community and, finally, the incorporation of schemes that explain the fundamentals of the main learnings considered when generating ELM-based trained smart models, which can be useful for medical image processing.spa
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2023.126618
dc.identifier.issn09252312 (Electrónico)
dc.identifier.urihttps://hdl.handle.net/20.500.12442/16113
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0925231223007415
dc.language.isoeng
dc.publisherELSEVIERspa
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Stateseng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.sourceNeurocomputingeng
dc.sourceVol. 556 (2023)spa
dc.subject.keywordsExtreme learning machineeng
dc.subject.keywordsMedical imagingeng
dc.subject.keywordsSupervised trainingeng
dc.subject.keywordsUnsupervised trainingeng
dc.subject.keywordsSemi-supervised trainingeng
dc.titleA comprehensive review of extreme learning machine on medical imagingspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.spaArtículo científico
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