Thyroid Cancer Detection Using Convolutional Neural Networks

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dc.authorscopusid59240058600
dc.authorscopusid36084505100
dc.authorscopusid8616530000
dc.contributor.authorIlkilic Aytac, Zeynep
dc.contributor.authorIseri, Ismail
dc.contributor.authorDandil, Besir
dc.date.accessioned2024-09-19T15:41:14Z
dc.date.available2024-09-19T15:41:14Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 -- 27 December 2023 through 28 December 2023 -- Zarqa -- 201143en_US
dc.description.abstractThyroid cancer is one of the most common cancers among all cancer types, and early diagnosis is very important to prevent the disease from progressing. This study shows that using Convolutional Neural Network (CNN), a deep learning model for the diagnosis of thyroid cancer, can provide faster and more accurate results for the diagnosis of thyroid cancer, especially in clinical trials. At the same time, this study highlights the potential applications of CNN models in medical imaging and cancer diagnosis. The dataset used in the CNN model is composed of thyroid biopsy images and divided into two classes: benign and malignant. The use of the CNN model on high-resolution thyroid biopsy images demonstrated that the model can effectively classify and a test accuracy of 84% was achieved. The model provided high sensitivity and specificity compared to similar studies. The findings show that the diagnosis process of thyroid cancer can be accelerated by using the CNN model, thus providing a better treatment process for cancer patients. © 2023 IEEE.en_US
dc.identifier.doi10.1109/EICEEAI60672.2023.10590377
dc.identifier.isbn979-835037336-3
dc.identifier.scopus2-s2.0-85199987287en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/EICEEAI60672.2023.10590377
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14115
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiopsy imagesen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectThyroid canceren_US
dc.titleThyroid Cancer Detection Using Convolutional Neural Networksen_US
dc.typeConference Objecten_US

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