Enhanced Diabetic Retinopathy Detection through Deep Learning Ensemble Models for Early Diagnosis


  • Deepak Dembla Department of Computer Application, JECRC: Jaipur Engineering College and Research Centre University, Jaipur, 303905, Rajasthan, India
  • Amita Meshram Department of Computer Science and Engineering, Yashwantrao Chavhan College of Engineering,Wanadongri, Nagpur, Maharashtra, India.
  • Anooja A. Department of Computer Application, JECRC: Jaipur Engineering College and Research Centre University, Jaipur, 303905, Rajasthan, India


Diabetic Retinopathy(DR), MobileNet, XceptionNet, ResNet50V2, DenseNet201, DenseNet169, InceptionV3


One of the most prevalent and primary causes of blindness associated with diabetes is diabetic retinopathy (DR). An early diagnosis of DR can stop the disease's progression. Vision impairment results from missed opportunities for diagnosis and treatment due to differences in the distribution of medical facilities. It is more effective and less expensive to classify and diagnose DR with better accuracy using neural network models. In order to enhance the functionality of DR classification models, This proposed model is an ensemble model that includes Transfer Learning algorithms that are Mobile Net, Xception, ResNet50V2, DenseNet201, DenseNet169, InceptionV3, and InceptionResNetV2   were added in this study for the detection of DR classification in fundus photographs. For that the APTOS and eyepacs dataset of a total of 5236 images are taken.This model taken 90% of images for training and 10% of images taken for testing on 30 epochs. The performance of DR classification can also be enhanced by adding extra 4 dense layers in each model and then ensemble that models. The output of the ensemble is applied for majority voting. The proposed ensemble model detected DR stages accurately and correctly. Hence  proposed Ensemble model accuracy is 92.23%.


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How to Cite

Dembla, D. ., Meshram, A. ., & A., A. . (2024). Enhanced Diabetic Retinopathy Detection through Deep Learning Ensemble Models for Early Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 26–38. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4706



Research Article