Enhanced Detection and Segmentation of Diabetic Retinopathy Lesions Using a Region of Interest Strategy
Abstract
Diabetic retinopathy is a common microvascular complication of diabetes that can lead to vision loss. Experts diagnose it by identifying retinal lesions, including hard exudates, soft exudates, hemorrhages, and microaneurysms, the earliest indicators. These lesions often appear near the macula, making this region crucial for early diagnosis. This study proposes a YOLOv12based approach for detecting and segmenting retinal lesions, focusing on the macula. We trained two models: one for region extraction using the Indian Diabetic Retinopathy Dataset and another for lesion detection with the Dataset for Diabetic Retinopathy. The model achieved a mean Average Precision of 0.4530 on the validation set and 0.3020 on the testing set for lesion segmentation.References
Alyoubi, W. L., Abulkhair, M. F., and Shalash, W. M. (2021). Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors, 21(11).
Elloumi, Y. and Kachouri, R. (2023). A robustness study of machine learning based methods for macula detection in pathological fundus images. In 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–6.
Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics yolov8.
Jocher, G. and Qiu, J. (2024). Ultralytics yolo11.
Kumar, N. S. and Ramaswamy Karthikeyan, B. (2021). Diabetic retinopathy detection using cnn, transformer and mlp based architectures. In Proc. of the 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pages 1–2, Hualien , Taiwan. IEEE.
Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., and Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501:511–522.
Munuera-Gifre, E., Saez, M., Juvinyà-Canals, D., Rodríguez-Poncelas, A., Barrot-de la–Puente, J.-F., Franch-Nadal, J., Romero-Aroca, P., Barceló, M. A., and Coll-de Tuero, G. (2020). Analysis of the location of retinal lesions in central retinographies of patients with type 2 diabetes. Acta Ophthalmologica, 98(1):e13–e21.
Murugan, R. and Roy, P. (2022). Micronet: microaneurysm detection in retinal fundus images using convolutional neural network. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 26(3):1057–1066.
of Ophthalmology, I. C. (2017). Updated 2017 ico guidelines for diabetic eye care. ICO Guidelines for Diabetic Eye Care, pages 1–33.
Ometto, G., Assheton, P., Calivá, F., Chudzik, P., Al-diri, B., Hunter, A., and Bek, T. (2017). Spatial distribution of early red lesions is a risk factor for development of vision-threatening diabetic retinopathy. Diabetologia, 60(12):2361–2367.
Pereira, A., Santos, C., Aguiar, M., Welfer, D., Dias, M., and Ribeiro, M. (2023). Improved detection of fundus lesions using yolor-csp architecture and slicing aided hyper inference. IEEE Latin America Transactions, 21(7):806–813.
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data, 3(3).
Santos, C., Aguiar, M., Welfer, D., and Belloni, B. (2022). A new approach for detecting fundus lesions using image processing and deep neural network architecture based on yolo model. Sensors, 22(17).
Santos, C., Aguiar, M., Welfer, D., Dias, M., Pereira, A., Ribeiro, M., and Belloni, B. (2023). A new approach for fundus lesions instance segmentation based on mask r-cnn x101-fpn pre-trained architecture. IEEE Access, 11:43603–43618.
Santos, C., De Aguiar, M. S., Welfer, D., and Belloni, B. (2021). Deep neural network model based on one-stage detector for identifying fundus lesions. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8, Shenzhen, China, 18–22 July 2021.
Tian, Y., Ye, Q., and Doermann, D. (2025). Yolov12: Attention-centric real-time object detectors.
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024a). Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458.
Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y. M. (2021). You only learn one representation: Unified network for multiple tasks.
Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y. M. (2024b). Yolov9: Learning what you want to learn using programmable gradient information.
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R. (2019). Detectron2. [link].
Elloumi, Y. and Kachouri, R. (2023). A robustness study of machine learning based methods for macula detection in pathological fundus images. In 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–6.
Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics yolov8.
Jocher, G. and Qiu, J. (2024). Ultralytics yolo11.
Kumar, N. S. and Ramaswamy Karthikeyan, B. (2021). Diabetic retinopathy detection using cnn, transformer and mlp based architectures. In Proc. of the 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pages 1–2, Hualien , Taiwan. IEEE.
Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., and Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501:511–522.
Munuera-Gifre, E., Saez, M., Juvinyà-Canals, D., Rodríguez-Poncelas, A., Barrot-de la–Puente, J.-F., Franch-Nadal, J., Romero-Aroca, P., Barceló, M. A., and Coll-de Tuero, G. (2020). Analysis of the location of retinal lesions in central retinographies of patients with type 2 diabetes. Acta Ophthalmologica, 98(1):e13–e21.
Murugan, R. and Roy, P. (2022). Micronet: microaneurysm detection in retinal fundus images using convolutional neural network. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 26(3):1057–1066.
of Ophthalmology, I. C. (2017). Updated 2017 ico guidelines for diabetic eye care. ICO Guidelines for Diabetic Eye Care, pages 1–33.
Ometto, G., Assheton, P., Calivá, F., Chudzik, P., Al-diri, B., Hunter, A., and Bek, T. (2017). Spatial distribution of early red lesions is a risk factor for development of vision-threatening diabetic retinopathy. Diabetologia, 60(12):2361–2367.
Pereira, A., Santos, C., Aguiar, M., Welfer, D., Dias, M., and Ribeiro, M. (2023). Improved detection of fundus lesions using yolor-csp architecture and slicing aided hyper inference. IEEE Latin America Transactions, 21(7):806–813.
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data, 3(3).
Santos, C., Aguiar, M., Welfer, D., and Belloni, B. (2022). A new approach for detecting fundus lesions using image processing and deep neural network architecture based on yolo model. Sensors, 22(17).
Santos, C., Aguiar, M., Welfer, D., Dias, M., Pereira, A., Ribeiro, M., and Belloni, B. (2023). A new approach for fundus lesions instance segmentation based on mask r-cnn x101-fpn pre-trained architecture. IEEE Access, 11:43603–43618.
Santos, C., De Aguiar, M. S., Welfer, D., and Belloni, B. (2021). Deep neural network model based on one-stage detector for identifying fundus lesions. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8, Shenzhen, China, 18–22 July 2021.
Tian, Y., Ye, Q., and Doermann, D. (2025). Yolov12: Attention-centric real-time object detectors.
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024a). Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458.
Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y. M. (2021). You only learn one representation: Unified network for multiple tasks.
Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y. M. (2024b). Yolov9: Learning what you want to learn using programmable gradient information.
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R. (2019). Detectron2. [link].
Published
2025-07-20
How to Cite
DIAS, Marcelo; SANTOS, Carlos; PEREIRA, Alejandro; AGUIAR, Marilton; WELFER, Daniel.
Enhanced Detection and Segmentation of Diabetic Retinopathy Lesions Using a Region of Interest Strategy. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 61-72.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.7288.
