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Diagnosing Diabetic Retinopathy by Using a Blood Vessel Extraction Technique and a Convolutional Neural Network

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Deep Learning for Medical Decision Support Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 909))

Abstract

As a critical relation with image processing and deep learning, it is a remarkable research way to work on medical images. As an important disease, diabetic retinopathy has the potential of being analyzed over medical images. Among adverse events associated with diabetes, there is the diabetic retinopathy as resulting to visual impairment if treatment deficiencies are not solved in long-term. Diabetic retinopathy (DR) is a critical eye disease as a result of the diabetes and is the most widely-seen factor of blindness for the countries in the developed-state.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Department of Computer Technologies, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Omer Deperlioglu

  3. Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan

    Jafar Alzubi

  4. Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

    Bogdan Patrut

Authors
  1. Utku Kose
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  2. Omer Deperlioglu
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  3. Jafar Alzubi
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  4. Bogdan Patrut
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Correspondence to Utku Kose .

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Kose, U., Deperlioglu, O., Alzubi, J., Patrut, B. (2021). Diagnosing Diabetic Retinopathy by Using a Blood Vessel Extraction Technique and a Convolutional Neural Network. In: Deep Learning for Medical Decision Support Systems. Studies in Computational Intelligence, vol 909. Springer, Singapore. https://doi.org/10.1007/978-981-15-6325-6_4

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