Classification Performance Evaluation on Diagnosis of Breast Cancer

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dc.authorscopusid57203003458
dc.authorscopusid56495320500
dc.contributor.authorBasarslan, M. S.
dc.contributor.authorKayaalp, F.
dc.date.accessioned2021-12-01T18:38:57Z
dc.date.available2021-12-01T18:38:57Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractCancer, which has many different types such as breast, pleural, and leukemia, is one of the common health problems of today. Most of them cause pain and treatment processes are so challenging. Medical authorities report that the diagnosis of cancer at early stages has a positive effect on medical treatments' success. On the way to design a computer-aided cancer diagnosis system about breast cancer to support the decisions of doctors about medical treatments, classification performances of six classifiers are investigated in this study. For this purpose, classifier models have been created with machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF); and deep learning algorithms such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory Network (LSTM). Two different open-source breast cancer datasets were used namely Wisconsin and Coimbra on experiments. Accuracy (Acc), Sensitivity (Sens), Precision (Pre), F-measure (F) were used as performance criteria. As a result of the tests, Acc values between 85% and 98% were obtained in the Coimbra breast cancer dataset; while Acc values were obtained between 92% and 97% in Wisconsin. According to the results obtained, it is seen that deep learning algorithms (RNN, GRU, and LSTM) are more successful than machine learning algorithms (SVM NB and RF). Among the deep learning algorithms, LSTM is more successful. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-79357-9_24
dc.identifier.endpage245en_US
dc.identifier.issn23674512
dc.identifier.scopus2-s2.0-85109834884en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage237en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-79357-9_24
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9927
dc.identifier.volume76en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes on Data Engineering and Communications Technologiesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast canceren_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.titleClassification Performance Evaluation on Diagnosis of Breast Canceren_US
dc.typeBook Chapteren_US

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