Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

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dc.authoridSharma, Bhisham/0000-0002-3400-3504
dc.authoridKadry, Seifedine/0000-0002-1939-4842
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridAggarwal, Ashutosh/0000-0002-3012-7833
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridMohammed, Mazin Abed/0000-0001-9030-8102
dc.authoridDamaševi?ius, Robertas/0000-0001-9990-1084
dc.authorwosidSharma, Bhisham/AAK-9369-2021
dc.authorwosidSharma, Bhisham/AAB-7076-2020
dc.authorwosidKadry, Seifedine/C-7437-2011
dc.authorwosidCengiz, Korhan/ABD-5559-2020
dc.authorwosidAggarwal, Ashutosh/P-9076-2015
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidMohammed, Mazin Abed/E-3910-2018
dc.contributor.authorLahoura, Vivek
dc.contributor.authorSingh, Harpreet
dc.contributor.authorAggarwal, Ashutosh
dc.contributor.authorSharma, Bhisham
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorDamasevicius, Robertas
dc.contributor.authorKathy, Seifedine
dc.date.accessioned2024-06-12T10:55:12Z
dc.date.available2024-06-12T10:55:12Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractGlobally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.en_US
dc.identifier.doi10.3390/diagnostics11020241
dc.identifier.issn2075-4418
dc.identifier.issue2en_US
dc.identifier.pmid33557132en_US
dc.identifier.scopus2-s2.0-85102471088en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics11020241
dc.identifier.urihttps://hdl.handle.net/20.500.14551/19336
dc.identifier.volume11en_US
dc.identifier.wosWOS:000622440800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast Canceren_US
dc.subjectExtreme Learning Machineen_US
dc.subjectCloud Computingen_US
dc.subjectTelehealthen_US
dc.subjectConvolutional Neural-Networken_US
dc.subjectSupport Vector Machineen_US
dc.subjectParkinsons-Diseaseen_US
dc.subjectClassificationen_US
dc.subjectFeaturesen_US
dc.subjectHybriden_US
dc.subjectSystemen_US
dc.subjectPredictionen_US
dc.titleCloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machineen_US
dc.typeArticleen_US

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