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• W3166799498 abstract "In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow." @default.
• W3166799498 created "2021-06-22" @default.
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• W3166799498 date "2021-05-30" @default.
• W3166799498 modified "2023-10-01" @default.
• W3166799498 title "Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms" @default.
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• W3166799498 doi "https://doi.org/10.1155/2021/9998819" @default.
• W3166799498 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8191587" @default.
• W3166799498 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34122785" @default.
• W3166799498 hasPublicationYear "2021" @default.
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