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COVİD-19 HASTALIĞININ TEŞHİSİNDE DERİN ÖĞRENME VE VERİ MAHREMİYETİ

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dc.creator CANBAY, Yavuz; KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
dc.creator İSMETOĞLU, Abdullah; KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ
dc.creator CANBAY, Pelin; KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
dc.date 2021-06-20T00:00:00Z
dc.date.accessioned 2021-12-03T11:45:43Z
dc.date.available 2021-12-03T11:45:43Z
dc.identifier https://dergipark.org.tr/tr/pub/jesd/issue/62893/870263
dc.identifier 10.21923/jesd.870263
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/93725
dc.description Covid-19 hastalığı, ortaya çıktığı günden bugüne birçok can kaybına yol açmıştır. Pandemi olarak ilan edilen bu hastalığa yakalanan kişilerde ciddi akciğer tahribatları oluşabilmektedir. Hekimlerin bu hastalığın teşhisinde akciğer özelinde çekilen bilgisayarlı tomografi (Computed Tomography - CT) ve X-Ray (Chest X-Ray - CXR) görüntülerini inceleyerek teşhis koydukları bilinmektedir. Bu CXR görüntülerinin çekildiği anda enfekte olduğu değerlendirilen kişilere hekim kontrolü öncesi yapılacak bir erken teşhis ile koruyucu önlemler hızlıca alınabilir ve hekimlerin hastalığı teşhis süreçleri kısaltılabilir. Diğer birçok hastalığın teşhisinde başarılı sonuçlar üreten yapay zekâ yöntemlerinin, Covid-19 hastalığında da başarılı sonuçlar ürettiği güncel çalışmalarda görülebilmektedir. Elde edilen başarılı sonuçların yanında, kullanılan sağlık verileri kişisel veri sınıfına girdiği için bu verilerin işlenmesinde ve analiz edilmesinde mahremiyet koruyucu önlemlere ihtiyaç olduğu açıktır. Gerek Kişisel Verileri Koruma Kanunu (KVKK) gerekse de Genel Veri Koruma Tüzüğü (General Data Protection Rule - GDPR), bu tür verilerin işlenmesinde mahremiyetin korunmasına özen gösterilmesi gerekliliğini ortaya koymaktadır. Bu çalışmada, Covid-19 hastalığını tespit eden yapay zekâ odaklı çalışmalar incelenmiş, kullanılan açık veri kümeleri sunulmuş, Covid-19 hastalığının tespitinde mahremiyeti dikkate alan çalışmalar gözden geçirilerek genel değerlendirmelerde bulunulmuştur.
dc.description Covid-19 disease has caused many mortalities since the day it emerged. People who suffer from this disease are more likely to have serious lung damages. It is known that physicians diagnose this disease by examining computed tomography (CT) and X-Ray (Chest X-Ray - CXR) images of the lung. At the moment these CXR images are taken, preventive measures can be taken quickly with an early diagnosis before physician control the people who are considered to be infected, and in addition, physicians' diagnosis processes can be shortened. It can be seen from the literature that artificial intelligence methods have produced successful results in the diagnosis of Covid-19 disease. Besides the successful results, it is a fact that since the health data is classified as personal data, privacy preserving measures are required in the processing and analysis of these data. Both Personal Data Protection Law and General Data Protection Rule (GDPR) reveal the need to focus on preserving privacy in the processing of these data. In this study, studies focusing on artificial intelligence to detect Covid-19 disease were examined, the open data sets used in the literature were presented, studies considering privacy in the detection of Covid-19 were investigated and general evaluations were presented.
dc.format application/pdf
dc.language tr
dc.publisher Süleyman Demirel Üniversitesi
dc.publisher Süleyman Demirel University
dc.relation https://dergipark.org.tr/tr/download/article-file/1540884
dc.source Volume: 9, Issue: 2 701-715 en-US
dc.source 1308-6693
dc.source Mühendislik Bilimleri ve Tasarım Dergisi
dc.subject Yapay Zekâ,Derin Öğrenme,Veri Mahremiyeti,Covid-19,Araştırma
dc.subject Artificial Intelligence,Deep Learning,Data Privacy,Covid-19,Review
dc.title COVİD-19 HASTALIĞININ TEŞHİSİNDE DERİN ÖĞRENME VE VERİ MAHREMİYETİ tr-TR
dc.title DEEP LEARNING AND DATA PRIVACY IN DIAGNOSIS OF COVID-19 en-US
dc.type info:eu-repo/semantics/article
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