Diabetic Retinopathy Classification Using ResNet50 and VGG-16 Pretrained Networks


  • Mustapha AATILA LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco
  • Mohamed LACHGAR LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco https://orcid.org/0000-0002-6155-3309
  • Hamid HRIMECH University Hassan 1st, ENSA of Berrechid, LAMSAD, B.P 218, Morocco
  • Ali KARTIT LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco


Diabetic retinopathy (DR), Deep learning, CNN, VGG-16, ResNet50


Diabetic retinopathy (DR) is considered one of the worldwide diseases of blindness, especially in the elderly. The main reason for this disease is the complication of diabetes in the retinal blood vessels. Usually, the warning signs are not observed. Screening is an important key to diagnosing the early stages of diabetic retinopathy. This work represents an intelligent system of DR classification based on deep learning (DL) tools, especially convolutional neural networks (CNN). Proposed system can assist ophthalmologists to make a preliminary decision, it allows a DR classification considering normal eyes, mild DR, Moderate DR, Severe DR and Proliferative DR. Obtained results, in terms of classification accuracy, for DR classification using the color retinal background images based on VGG-16 and ResNet50 models are in order 70% and 25% respectively.


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How to Cite

AATILA, M., LACHGAR, M. ., HRIMECH, H., & KARTIT, A. (2021). Diabetic Retinopathy Classification Using ResNet50 and VGG-16 Pretrained Networks. International Journal of Computer Engineering and Data Science (IJCEDS), 1(1), 1–7. Retrieved from http://ijceds.com/ijceds/article/view/15