Abstract
Diabetic retinopathy (DR) is a diabetes-related ocular complication that can lead to vision loss. Its detection is performed through fundus examinations, assisted by lesion segmentation techniques. The IDRiD and APTOS-2019 datasets are used for DR lesion segmentation and classification, respectively. Using the U-Net architecture, lesions such as microaneurysms and exudates were segmented, while CNNs classified disease stages. In this paper, we present DR-AIVis, an approach for segmentation, classification, and explainability of diabetic retinopathy (DR). Our results demonstrate an accuracy of 93.84% in segmentation and 98.30% in classification. Additionally, we employ Grad-CAM to highlight the most relevant regions of the image. As contributions, our work includes an automated system for DR segmentation and classification, as well as a mechanism to identify the most important image regions for decision-making, thereby enhancing confidence in the provided results using Grad-CAM.
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Acknowledgment
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. Also, Pedro Pedrosa Rebouças Filho acknowledges the sponsorship from the Brazilian National Council for Research and Development (CNPq) via Grant 301455/2022-8 and State Foundation for the Support of Scientific and Technological Development (FUNCAP) for the financial support via grants 08/2023 and 09/2023.
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da Silva, M.C., Silva, C.M., da Silva, S.P.P., Sarmento, R.M., Song, H.H., Filho, P.P.R. (2026). DR-AIVis: A Hybrid Approach for Diabetic Retinopathy Detection Using U-Net Segmentation and CNN Classification with Grad-CAM Explainability. In: de Freitas, R., Furtado, D. (eds) Intelligent Systems. BRACIS 2025. Lecture Notes in Computer Science(), vol 16181. Springer, Cham. https://doi.org/10.1007/978-3-032-15990-8_10
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DOI: https://doi.org/10.1007/978-3-032-15990-8_10
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