A hybrid feature fusion for retinal OCT image classification using traditional and deep learning methods
Yadav, Mithilesh Kumar Singh (Dr B R Ambedkar National Institute of Technology (Índia))
Singh, Nagendra Pratap (Dr B R Ambedkar National Institute of Technology (Índia))

Fecha:	2026
Resumen:	Early and accurate detection of diabetic macular edema (DME) is essential to avoid permanent loss of vision. This paper introduces Fusion-WideNet, a new hybrid classification model that combines handcrafted and deep features for the analysis of retinal OCT images. Handcrafted features-Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) are learned to extract local textural information and deep semantic features extracted from a pretrained ResNet50 convolutional neural network. These two feature sets are combined in high-dimensional space and fed into a Wide Neural Network (WideNet), which is a shallow but high-capacity network designed to handle large feature vectors. The model achieves a classification accuracy of 99. 5%, outperforming traditional models and deep-only baselines. The proposed Fusion-WideNet framework not only demonstrates high diagnostic performance but also provides interpretability essential for real-world ophthalmic screening and clinical decision support.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	ELCVIA, Vol. 25, Num. 1 (2026) , p. 43-59 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/2280
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000007325
DOI: 10.5565/rev/elcvia.2280

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