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Dergiler / Journal of Scientific Reports-A / 2022 / Sayı: 051

A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY

Gökalp ÇINARER
Kazım KILIÇ
Tuba PARLAR
Sayfa
106–119
Yayın Tarihi
2022-12-31
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Diabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss.
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Temel Bulgular & Veri:The accurate diagnosis of DR stages is highly important in guiding the treatment process.
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Sonuç & Katkı:Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Convolutional neural networks · CNNs · Deep learning

Abstract

Diabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss. Diabetic retinopathy (DR) is the most common form of diabetes-caused eye disease. Early diagnosis and treatment are crucial to prevent vision loss. DR is a progressive disease composed of five stages. The accurate diagnosis of DR stages is highly important in guiding the treatment process. In this study, we propose a deep transfer learning framework for automatic detection of DR stages. We examine our proposed model by comparing different convolutional neural networks architectures: VGGNet19, DenseNet201, and ResNet152. Our results demonstrate better accuracy after applying transfer learning and hyper-parameter tuning to classify the fundus images. When the general test accuracy and the performance evaluations are compared, the DenseNet201 model is observed with the highest test accuracy of 82.7%. Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.

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Bu Makaleye Atıf Yap

ÇINARER, G., KILIÇ, K., PARLAR, T. (2022). A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY. Journal of Scientific Reports-A, Sayı: 051, 106–119. https://ajindex.com/articles/858492

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Kaynakça (14)

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