Tıp Bilişimi IV

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BÖLÜM


DOI :10.26650/B/T3.2024.40.009   IUP :10.26650/B/T3.2024.40.009    Tam Metin (PDF)

Deri̇n Öğrenme Yöntemleri̇ni̇n Sağlık Alanında Uygulamaları: Di̇yabeti̇k Reti̇nopati̇ Tahmi̇ni̇

Cihan Çiftçi, Halim Kazan

Uzun yıllardır tıbbi görüntüleme teknikleri hastalıkların tanı ve tedavisinde önemli bir araç olarak kullanılmaktadır. Makine öğrenmesinin bir alt dalı olan derin öğrenme yöntemleri, hastalıkların erken teşhisinde, tıbbi görüntüleme araçlarından elde edilen görüntülerin hızlı yorumlanmasında, tıp profesyonellerinin iş yükünün azaltılmasında, karar verme noktasında yaşanacak anlaşmazlıklara dair içgörü sağlanmasında ve doğru kararlar verilmesinde önemli rol oynamaktadır. Sağlık alanında farklı kaynaklardan elde edilen heterojen büyük verilerin yorumlanması ve analizinde, görüntü işleme alanında günümüzde geleneksel yöntemlere göre daha güçlü bir araç olarak derin öğrenme yöntemleri kullanılmaktadır. Son yıllarda hastalık tespitinde görüntü işlemede elde edilen büyük verilerin analizinde derin öğrenme yöntemleri giderek daha fazla odak noktası haline gelmiştir. Bu çalışmada görüntü işleme alanında derin öğrenme yöntemlerinin hastalıkların erken teşhisinde kullanım alanları incelenmiştir. Ayrıca çalışmada diyabetik retinopatinin erken teşhisinde derin öğrenme modelleri oluşturulmuştur. APTOS 2019 Körlük Tespiti’nden elde edilen görüntü verileri diyabetik retinopati hastalığının tanısında kullanılmıştır. Elde edilen sonuçlara göre derin öğrenme modellerinin görüntü işlemede önemli bir yöntem olduğu ve diyabetik retinopatinin erken tanısında kullanımının tıp profesyonelleri için ciddi bir araç olacağı sonucuna varılmıştır.


Anahtar Kelimeler: Derin öğrenme, görüntü işleme, sağlık uygulamaları, erken teşhis
DOI :10.26650/B/T3.2024.40.009   IUP :10.26650/B/T3.2024.40.009    Tam Metin (PDF)

Applications of Deep Learning Methods in the Field of Health: Prediction of Diabetic Retinopathy

Cihan Çiftçi, Halim Kazan

For many years, medical imaging techniques have been used as an important tool in the diagnosis and treatment of diseases. Deep learning methods, which are a sub-branch of machine learning, play an important role in the early diagnosis of diseases, in the rapid interpretation of images obtained from medical imaging tools, in reducing the workload of medical professionals, providing insights in the disagreements to be experienced at the point of decision-making, and making the right decisions. In the management, interpretation and analysis of heterogeneous big data obtained from different sources in the field of health, deep learning methods are used today as a more powerful tool than traditional methods in the field of image processing. In recent years, deep learning methods have increasingly become the focus in the analysis of big data obtained in image processing in disease detection. In this study, the use of deep learning methods in the field of image processing in the early diagnosis of diseases has been examined. In addition, deep learning models were used in the early diagnosis of diabetic retinopathy in the study. The image data obtained from APTOS 2019 Blindness Detection was used in the diagnosis of diabetic retinopathy disease. According to the results obtained, it was concluded that deep learning models are an important method in image processing, and its use in the early diagnosis of diabetic retinopathy will be a serious tool for medical professionals.


Anahtar Kelimeler: Deep learning, image processing, health applications, early diagnosis

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