Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8614
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dc.contributor.authorMikhailov N.-
dc.contributor.authorShakeel M.-
dc.contributor.authorUrmanov A.-
dc.contributor.authorLee M.-H.-
dc.contributor.authorDemirci M.F.-
dc.date.accessioned2022-07-30T16:43:34Z-
dc.date.available2022-07-30T16:43:34Z-
dc.date.issued2021-
dc.identifier.citationMikhailov, N., Shakeel, M., Urmanov, A., Lee, M. H., & Demirci, M. F. (2021, November). Optimization of CNN Model for Breast Cancer Classification. In 2021 16th International Conference on Electronics Computer and Computation (ICECCO) (pp. 1-3). IEEE.en_US
dc.identifier.isbn9781665409452-
dc.identifier.urihttps://doi.org/10.1109/ICECCO53203.2021.9663847-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8614-
dc.description16th International Conference on Electronics Computer and Computation, ICECCO 2021 -- 25 November 2021 through 26 November 2021 -- -- 176250en_US
dc.description.abstractApplication of deep learning techniques for breast cancer classification using histopathology images has gained interest during recent years. In this study, an open-source convolutional neural network (CNN) model developed for breast cancer classification model is optimized by performing sensitivities on various CNN parameters such as data balancing, activation functions and adding/removing CNN layers. Some of the parameters are less-sensitive in affecting model's performance. The results show that by balancing the number of positive and negative samples, accuracy of the model can be improved. However, some additional work is required to reach to that point. Furthermore, the computation time is reduced by almost 30% by increasing the learning rate from 0.01 to 0.05 while the training and validation accuracy and loss are comparable to that of the original CNN model. © 2021 IEEE.en_US
dc.description.sponsorshipNazarbayev University, NUen_US
dc.description.sponsorshipV. ACKNOWLEDGEMENT This work was supported by Faculty Development Competitive Research Grant Program (No. 080420FD1909) at Nazarbayev University.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2021 16th International Conference on Electronics Computer and Computation, ICECCO 2021en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectactivation functionen_US
dc.subjectbreast canceren_US
dc.subjectconvolutional neural networken_US
dc.subjectdata balancingen_US
dc.subjectdeep learningen_US
dc.subjectChemical activationen_US
dc.subjectConvolutionen_US
dc.subjectDeep learningen_US
dc.subjectDiseasesen_US
dc.subjectMedical imagingen_US
dc.subjectMultilayer neural networksen_US
dc.subjectActivation functionsen_US
dc.subjectBreast Canceren_US
dc.subjectBreast cancer classificationsen_US
dc.subjectConvolutional neural networken_US
dc.subjectData balancingen_US
dc.subjectDeep learningen_US
dc.subjectLearning techniquesen_US
dc.subjectNeural network modelen_US
dc.subjectOpen-sourceen_US
dc.subjectOptimisationsen_US
dc.subjectConvolutional neural networksen_US
dc.titleOptimization of CNN Model for Breast Cancer Classificationen_US
dc.typeConference Objecten_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.identifier.scopus2-s2.0-85124972293en_US
dc.institutionauthorDemirci, Muhammed Fatih-
dc.identifier.doi10.1109/ICECCO53203.2021.9663847-
dc.authorscopusid57460155800-
dc.authorscopusid57218880784-
dc.authorscopusid57460414600-
dc.authorscopusid56872008300-
dc.authorscopusid14041575400-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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