Assessing ResNeXt and RegNet Models for Diabetic Retinopathy Classification: A Comprehensive Comparative Study

<b>Background/Objectives:</b> Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-...

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Hauptverfasser:	Samara Acosta-Jiménez, Valeria Maeda-Gutiérrez, Carlos E. Galván-Tejada, Miguel M. Mendoza-Mendoza, Luis C. Reveles-Gómez, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Antonio García-Domínguez
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2025-08-01
Schriftenreihe:	Diagnostics
Schlagworte:	diabetic retinopathy deep learning convolutional neural network ResNeXt RegNet SHAP
Online-Zugang:	https://www.mdpi.com/2075-4418/15/15/1966
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