Comparison of Transfer Learning Strategies for Diabetic Retinopathy Detection

Karaca, Busra Kubra; Oltu, Burcu; Özgür, Atilla; Erdem, Hamıt

doi:10.1109/asyu52992.2021.9599002

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“…Transfer learning has successfully been applied in other areas of research, e.g. quality prediction (Liu et al, 2019) and medical applications (Yi and Wang, 2021;Kübra Karaca et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Passive acoustic monitoring of animal populations with transfer learning

Dufourq

Batist

Foquet³

et al. 2022

Ecological Informatics

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Section: Introductionmentioning

confidence: 99%

Passive acoustic monitoring of animal populations with transfer learning

Dufourq

Batist

Foquet³

et al. 2022

Ecological Informatics

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A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection

Oltu

Karaca

Erdem

et al. 2023

Gazi University Journal of Science

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Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 43 publications between 2015 and 2021. The published papers are summarized using 3 figures and 10 tables, giving information about 29 pre-trained CNN models, 13 DR data sets and standard performance metrics.

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Comparison of Transfer Learning Strategies for Diabetic Retinopathy Detection

Cited by 2 publications

References 14 publications

Passive acoustic monitoring of animal populations with transfer learning

Passive acoustic monitoring of animal populations with transfer learning

A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection

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