TY  - JOUR
T1  - Classification of Diabetic Retinopathy Disease with Deep Learning Methods
AU  - Tuncel, Metin
AU  - Uçar, Murat
PY  - 2025
DA  - May
Y2  - 2025
JF  - Artificial Intelligence Theory and Applications
JO  - AITA
PB  - İzmir Bakırçay Üniversitesi
WT  - DergiPark
SN  - 2757-9778
SP  - 1
EP  - 17
VL  - 5
IS  - 1
LA  - en
AB  - Diabetes is defined as a chronic disease that occurs as a result of an increase in blood sugar level (hyperglycemia), in which the organism cannot make sufficient use of carbohydrates, fats and proteins due to the inability of the pancreas to produce enough insulin hormone or the inability of this hormone to function. According to the chronic diseases report published by the World Health Organisation, diabetes ranks first in terms of intensity. One of the complications of type 1 diabetes is that it causes diabetic retinopathy. Diabetic retinopathy is defined as an eye condition caused by damage to the blood vessels in the light-sensitive tissue (retina) located at the back of the eye due to diabetes complications. According to the International Diabetes Federation (2021) Diabetes Atlas 10th Edition, diabetes is among the top three diseases that cause blindness. Blindness caused by diabetes is mostly caused by the destruction of small vessels in the retina due to long-term hyperglycemia. Approximately 25% of diabetic patients worldwide have diabetic retinopathy at any level. There are approximately 2 million diabetic patients in our country and 25% of these patients have diabetic retinopathy.  There are 5 classes of diabetic retinopathy. These are non-proliferative diabetic retinopathy (npdr), mild non-proliferative retinopathy, moderate non-proliferative retinopathy, severe non-proliferative retinopathy, proliferative diabetic retinopathy (pdr) from the lowest to the most severe. In this study, using the APTOS2019 dataset, a computer-aided diagnosis system is created to help doctors make early diagnosis with convolution-based deep learning models. Two- and five-class classification was performed using state of the art models VGG16, InceptionResNetV2, ResNet152V2, EfficientNetB0, MobileNetV2, which are frequently preferred in the classification of medical images in the literature. Since the amount of data in the five-class classification in diabetic retinopathy disease images was not equal, the data were equalised by using data augmentation techniques using the albumentations library in the training dataset. Among the state of the art models used in the two-class classification, VGG16 was the best model since its accuracy, precision, sensitivity and f1-score metric values were 0.97. Among the models used in five-class classification, VGG16 was the best model due to its accuracy, precision, sensitivity and f1-score metric values of 0.78 and precision 0.79.
KW  - Diabetic Retinopathy
KW  - Deep Learning
KW  - Convolutional Neural Networks
KW  - Transfer Learning
CR  - [1] Coşansu, G. (2015). Diyabet: Küresel bir salgın hastalık. Okmeydanı Tıp Dergisi, 31, 1-6.
CR  - [2] World Health Organization. (2021). Diabetes. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/diabetes
CR  - [3] International Diabetes Federation. (2021). IDF Diabetes Atlas | Tenth Edition. Retrieved from https://www.idf.org/news/240:diabetes-now-affects-one-in-10-adults-worldwide.html and https://diabetesatlas.org/atlas/tenth-edition/
CR  - [4] Grosman, B., Ilany, J., Roy, A., Kurtz, N., Wu, D., Parikh, N., Voskanyan, G., Konvalina, N., Mylonas, C., Gottlieb, R., Kaufman, F., & Cohen, O. (2016). Hybrid closed-loop insulin delivery in type 1 diabetes during supervised outpatient conditions. Journal of Diabetes Science and Technology, 10(3), 708–713. https://doi.org/10.1177/1932296816631568
CR  - [5] Swapna, G., Vinayakumar, R., & Soman, K. P. (2018). Diabetes detection using deep learning algorithms. ICT Express, 4(4), 243–246. https://doi.org/10.1016/j.icte.2018.10.005
CR  - [6] Atwany, M. Z., Sahyoun, A. H., & Yaqub, M. (2022). Deep learning techniques for diabetic retinopathy classification: A survey. IEEE Access, 10, 28642-28655. https://doi.org/10.1109/ACCESS.2022.3157632
CR  - [7] Ağca, K. (2022).  Evrişimsel sinir ağları kullanarak diyabetik retinopati hastalığının tespiti [Yüksek lisans tezi, Sivas Cumhuriyet Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr
CR  - [8] İnan, S. (2014). Diabetik retinopati ve etiyopatogenezi. Kocatepe Tıp Dergisi, 15(2), 207-217.
CR  - [9] Cunha, J. P. (2021). What are the stages of diabetic retinopathy? eMedicineHealth. Retrieved from https://www.emedicinehealth.com/what_are_the_stages_of_diabetic_retinopathy/article_em.htm
CR  - [10] Yakar, H. K. (2018). Yaşlılıkta Diyabetin Diğer Bir Yüzü: Diyabetik Retinopati Ve Düşmeler. Izmir Democracy University Health Sciences Journal, 1(2), 13-22.
CR  - [11] APTOS. (2019). Blindness detection. Kaggle. Retrieved from https://www.kaggle.com/c/aptos2019-blindness-detection/overview/aptos-2019
CR  - [12] Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410. https://doi.org/10.1001/jama.2016.17216
CR  - [13] Lee, C. S., Baughman, D. M., & Lee, A. Y. (2017). Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmology Retina, 1(4), 322–327. https://doi.org/10.1016/j.oret.2016.12.009
CR  - [14] Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., & Liu, J. (2015, August). Glaucoma detection based on deep convolutional neural network. In 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 715-718). IEEE.
CR  - [15]	Uçar, M. (2021). Glokom Hastalığının Evrişimli Sinir Ağı Mimarileri ile Tespiti. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 23(68), 521-529.
CR  - [16]	Cao, X., Lin, J., Gao, X., & Li, Z. (2024). Integrating convolution and transformer for enhanced diabetic retinopathy detection. International Journal of Bio-Inspired Computation (IJBIC), 23(4). https://doi.org/10.1504/IJBIC.2024.139257
CR  - [17] 	Chandra, R., Tiwari, S., Kumar, S. S., & Agarwal, S. (2024). Diabetic retinopathy prediction based on CNN and AlexNet model. In 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 382–387). IEEE. https://doi.org/10.1109/Confluence60223.2024.10463351
CR  - [18]	Ohri, K., Kumar, M., & Sukheja, D. (2023). Self-supervised approach for diabetic retinopathy severity detection using vision transformer. Pattern Analysis and Applications. https://doi.org/10.1007/s13748-024-00325-0
CR  - [19]	Oulhadj, M., Riffi, J., Khodriss, C., Mahraz, A. M., Yahyaouy, A., Abdellaoui, M., Andaloussi, I. B., & Tairi, H. (2024). Diabetic retinopathy prediction based on vision transformer and modified capsule network. Computers in Biology and Medicine, 175, 108523. https://doi.org/10.1016/j.compbiomed.2024.108523
CR  - [20] Mondal, S. S., Mandal, N., Singh, K. K., Singh, A., & Izonin, I. (2023). EDLDR: An ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics, 13(1), 124. https://doi.org/10.3390/diagnostics13010124
CR  - [21]	Vijayan, M., & Venkatakrishnan, S. (2023). A regression-based approach to diabetic retinopathy diagnosis using EfficientNet. Diagnostics, 13(4), 774. https://doi.org/10.3390/diagnostics13040774.
CR  - [22]	Oulhadj, M., Riffi, J., Khodriss, C., Mahraz, A. M., Bennis, A., Yahyaouy, A., ... & Tairi, H. (2023). Diabetic retinopathy prediction based on wavelet decomposition and modified capsule network. Journal of Digital Imaging, 36(4), 1739-1751.  https://doi.org/10.1007/s10278-023-00813-0
CR  - [23] Oulhadj, M., Riffi, J., Khodriss, C., Mahraz, A. M., Bennis, A., Yahyaouy, A., ... & Tairi, H. (2023, January). Diabetic Retinopathy Prediction Based on Transfer Learning and Ensemble Voting. In International Conference on Digital Technologies and Applications (pp. 929-937). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-29857-8_92
CR  - [24] Oulhadj, M., Riffi, J., Chaimae, K., Mahraz, A. M., Ahmed, B., Yahyaouy, A., ... & Tairi, H. (2022). Diabetic retinopathy prediction based on deep learning and deformable registration. Multimedia Tools and Applications, 81(20), 28709-28727.  https://doi.org/10.1007/s11042-022-12968-z
CR  - [25] Islam, M. R., Abdulrazak, L. F., Nahiduzzaman, M., Goni, M. O. F., Anower, M. S., Ahsan, M., ... & Kowalski, M. (2022). Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Computers in biology and medicine, 146, 105602. https://doi.org/10.1016/j.compbiomed.2022.105602
CR  - [26] Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations (ICLR 2015) (pp. 1–14).
CR  - [27] Sharma, P., Nayak, D. R., Balabantaray, B. K., Tanveer, M., & Nayak, R. (2024). A survey on cancer detection via convolutional neural networks: Current challenges and future directions. Neural Networks, 169, 637-659. https://doi.org/10.1016/j.neunet.2023.11.006.
CR  - [28] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).
CR  - [29] Tragoudaras, A., Stoikos, P., Fanaras, K., Tziouvaras, A., Floros, G., Dimitriou, G., ... & Stamoulis, G. (2022). 
Design space exploration of a sparse mobilenetv2 using high-level synthesis and sparse matrix techniques on FPGAs. Sensors, 22(12), 4318.  https://doi.org/10.3390/s22124318
CR  - [30] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
CR  - [31] Vatanpour, M., & Haddadnia, J. (2024). Brain tumour segmentation of MR images based on custom attention mechanism with transfer‐learning. IET Image Processing, 18(4), 886-896. 
https://doi.org/10.1049/ipr2.12992
CR  - [32] Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017, February). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1). https://doi.org/10.1609/aaai.v31i1.11231
CR  - [33] Peng, C., Liu, Y., Yuan, X., & Chen, Q. (2022). Research of image recognition method based on enhanced inception-ResNet-V2. Multimedia Tools and Applications, 81(24), 34345-34365. https://doi.org/10.1007/s11042-022-12387-0
CR  - [34] Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR. http://proceedings.mlr.press/v97/tan19a.html
CR  - [35] Montalbo, F. J. P., & Alon, A. S. (2021). Empirical analysis of a fine-tuned deep convolutional model in classifying and detecting malaria parasites from blood smears. KSII Transactions on Internet and Information Systems (TIIS), 15(1), 147-165. https://doi.org/10.3837/tiis.2021.01.009
CR  - [36] Mondal, S. S., Mandal, N., Singh, K. K., Singh, A., & Izonin, I. (2022). Edldr: An ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics, 13(1), 124. https://doi.org/10.3390/diagnostics13010124
CR  - [37] Oulhadj, M., Riffi, J., Khodriss, C., Mahraz, A. M., Yahyaouy, A., Abdellaoui, M., ... & Tairi, H. (2024). Diabetic retinopathy prediction based on vision transformer and modified capsule network. Computers in Biology and Medicine, 175, 108523. https://doi.org/10.1016/j.compbiomed.2024.108523.
CR  - [38] Bodapati, J. D. (2022). Stacked convolutional auto-encoder representations with spatial attention for efficient diabetic retinopathy diagnosis. Multimedia Tools and Applications, 81(22), 32033-32056. https://doi.org/10.1007/s11042-022-12811-5
CR  - [39] Zhao, S., Wu, Y., Tong, M., Yao, Y., Qian, W., & Qi, S. (2022). Cot-xnet: contextual transformer with xception network for diabetic retinopathy grading. Physics in Medicine & Biology, 67(24), 245003.  https://doi.org/10.1088/1361-6560/ac9fa0
CR  - [40] Shaik, N. S., & Cherukuri, T. K. (2022). Hinge attention network: A joint model for diabetic retinopathy severity grading. Applied Intelligence, 52(13), 15105-15121.  https://doi.org/10.1007/s10489-021-03043-5
CR  - [41] Hu, J., Wang, H., Wang, L., & Lu, Y. (2022). Graph adversarial transfer learning for diabetic retinopathy classification. IEEE Access, 10, 119071-119083. https://doi.org/10.1109/ACCESS.2022.3220776
CR  - [42] Fan, R., Liu, Y., & Zhang, R. (2021). Multi-scale feature fusion with adaptive weighting for diabetic retinopathy severity classification. Electronics, 10(12), 1369.
CR  - [43] Sugeno, A., Ishikawa, Y., Ohshima, T., & Muramatsu, R. (2021). Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning. Computers in Biology and Medicine, 137, 104795.
CR  - [44] Al-Antary, M. T., & Arafa, Y. (2021). Multi-scale attention network for diabetic retinopathy classification. IEEE Access, 9, 54190-54200.
CR  - [45] Kumar, G., Chatterjee, S., & Chattopadhyay, C. (2021). DRISTI: a hybrid deep neural network for diabetic retinopathy diagnosis. Signal, Image and Video Processing, 15(8), 1679-1686.
UR  - https://dergipark.org.tr/en/pub/aita/issue//1596601
L1  - https://dergipark.org.tr/en/download/article-file/4418127
ER  -