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https://hdl.handle.net/20.500.13091/1070
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DC Field | Value | Language |
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dc.contributor.author | Örnek, Ahmet Haydar | - |
dc.contributor.author | Ceylan, Murat | - |
dc.date.accessioned | 2021-12-13T10:34:38Z | - |
dc.date.available | 2021-12-13T10:34:38Z | - |
dc.date.issued | 2019 | - |
dc.identifier.isbn | 978-1-7281-1864-2 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1070 | - |
dc.description | 42nd International Conference on Telecommunications and Signal Processing (TSP) -- JUL 01-03, 2019 -- Budapest, HUNGARY | en_US |
dc.description.abstract | Convolutional neural networks (CNN) models demonstrate high performance in image-based applications such as image classification, segmentation, noise reduction and object recognition. However, balanced and sufficient data are required to effectively train a CNN model but this is not always possible. Since conditions of both hospitals and patients are not always appropriate for collecting data and patients with the same disease are not always available, the problem of collecting balanced and sufficient data are often occurred in medical fields. In this study, comparison of traditional data augmentation methods such as rotating, mirroring, zooming, shearing, histogram equalization, color changing, sharpening, blurring, brightness enhancement and contrast changing were performed by using neonatal thermal images. These images belonged to 19 unhealthy and 19 healthy neonates were obtained from Selcuk University, Faculty of Medicine, Neonatal Intensive Care Unit. A combination of three different augmentation methods were implemented to original images in each one of the 10 different comparisons accomplished and CNN was used to classify the these comparisons in the study. When contrast changing, sharpening and blurring methods were used, increased the sensitivity rate by 23.49%, the specificity rate by 29.09% and the accuracy rate by 26.29%. The obtained results show that simple and low-cost traditional data enhancement methods can improve the performance of classification. | en_US |
dc.description.sponsorship | IEEE Reg 8, IEEE Hungary Sect, IEEE Czechoslovakia Sect & SP CAS COM Joint Chapter, Sci Assoc Infocommunicat, Brno Univ Technol, Dept Telecommunicat, Budapest Univ Technol & Econ, Dept Telecommunicat & Media Informat, Czech Tech Univ Prague, Dept Telecommunicat Engn, Isik Univ, Dept Elect & Elect Engn,, Istanbul Tech Univ, Elect & Communicat Engn Dept, Josip Juraj Strossmayer Univ Osijek, Fac Elect Engn Comp Sci & Informat Technol, Karadeniz Tech Univ, Dept Elect & Elect Engn, Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn, Seikei Univ, Grad Sch & Fac Sci & Technol, Informat Networking Lab, Slovak Univ Technol Bratislava, Inst Multimedia Informat & Commun Technologies, Escola Univ Politecnica Mataro, Tecnocampus, Tech Univ Sofia, Fac Telecommunicat, Univ Paris 8, UFR MITSIC, Lab Informatique Avancee Saint Denis, Univ Politehnica Bucharest, Ctr Adv Res New Mat Prod & Innovat Proc, Univ Ljubljana, Lab Telecommunicat, Univ Patras, Phys Dept, VSB Tech Univ Ostrava, Dept Telecommunicat, W Pomeranian Univ Technol, Fac Elect Engn | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [215E019] | en_US |
dc.description.sponsorship | This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019). | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2019 42ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | classification | en_US |
dc.subject | convolutional neural networks | en_US |
dc.subject | data augmentation | en_US |
dc.subject | medical thermography | en_US |
dc.subject | neonate | en_US |
dc.subject | TEMPERATURE | en_US |
dc.title | Comparison of Traditional Transformations for Data Augmentation in Deep Learning of Medical Thermography | en_US |
dc.type | Conference Object | en_US |
dc.identifier.scopus | 2-s2.0-85071065674 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.startpage | 191 | en_US |
dc.identifier.endpage | 194 | en_US |
dc.identifier.wos | WOS:000493442800042 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | Conference Object | - |
item.grantfulltext | embargo_20300101 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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Comparison_of_Traditional_Transformations_for_Data_Augmentation_in_Deep_Learning_of_Medical_Thermography.pdf Until 2030-01-01 | 189.82 kB | Adobe PDF | View/Open Request a copy |
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