Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection

•Our study introduces a novel approach integrating generative adversarial networks (GANs) to improve class distribution and generalization in diabetic retinopathy classification tasks, advancing beyond prior works that predominantly focused on segmentation or synthesis.•The dual-stage enhancement co...

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Bibliographic Details
Published in:	Photodiagnosis and photodynamic therapy Vol. 53; p. 104552
Main Authors:	GENCER, Kerem, GENCER, Gülcan, CERAN, Tuğçe Horozoğlu, BİLİR, Aynur ER, DOĞAN, Mustafa
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01.06.2025 Elsevier
Subjects:	Autoencoder Deep Learning Denoising autoencoders Diabetic retinopathy Diabetic Retinopathy - diagnosis Diabetic Retinopathy - diagnostic imaging Generative Adversarial Networks Generative adversarial networks (GANs) Hematology, Oncology, and Palliative Medicine Humans Image Interpretation, Computer-Assisted - methods Internal Medicine Medical image analysis Neural Networks, Computer Photodiagnosis Sensitivity and Specificity Tomography, Optical Coherence - methods Transfer learning Diabetic retinopathy Transfer learning Denoising autoencoders Photodiagnosis Medical image analysis Generative adversarial networks (GANs)
ISSN:	1572-1000, 1873-1597, 1873-1597
Online Access:	Get full text
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