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. 2025 Apr;263(4):935-956.
doi: 10.1007/s00417-024-06687-4. Epub 2024 Dec 16.

Enhancing diabetic retinopathy and macular edema detection through multi scale feature fusion using deep learning model

Gowri L  1 , Haris R  1 , Sumathi M  2 , S P Raja  3
Affiliations

Enhancing diabetic retinopathy and macular edema detection through multi scale feature fusion using deep learning model

Gowri L et al. Graefes Arch Clin Exp Ophthalmol. 2025 Apr.

Abstract

Background: This work tackles the growing problem of early identification of diabetic retinopathy and diabetic macular edema. The deep neural network design utilizes multi-scale feature fusion to improve automated diagnostic accuracy. Methods This approach uses convolutional neural networks (CNN) and is designed to combine higher-level semantic inputs with low-level textural characteristics. The contextual and localized abstract representations that complement each other are combined via a unique fusion technique.

Results: Use the MESSIDOR dataset, which comprises retinal images labeled with pathological annotations, for model training and validation to ensure robust algorithm development. The suggested model shows a 98% general precision and good performance in diabetic retinopathy. This model achieves an impressive nearly 100% exactness for diabetic macular edema, with particularly high accuracy (0.99).

Conclusion: Consistent performance increases the likelihood that the vision will be upheld through public screening and extensive clinical integration.

Keywords: Deep Learning; Diabetic Macular Edema; Diabetic Retinopathy; Feature Fusion; MESSIDOR.

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Conflict of interest statement

Declarations. Human Ethics and Consent: Not applicable. We don’t involve humans and animals for our research. Consent to Publish: Not applicable. Conflict of Interest: We declare that there is no conflict of interest. Ethical Approval: Not applicable. We don’t involve humans and animals for our research. (And/or in case humans were involved). This article does not contain any studies with human participants or animals performed by any of the authors. (In case humans are involved) Informed Consent: N/A.

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