CLAHE-AlexNet optimized deep learning model for accurate detection of diabetic retinopathy
Swetha G. Gupta G. Rane K.P. Ghag O.M. Korde S.K. Lalar S. Omarov B. Raghuvanshi A.
August 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 42752 - 2761 pp.
Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the retina. Loss of vision due to diabetes is a common consequence of the illness and a key factor in the progression of vision loss and blindness. Both ophthalmology and diabetes research have become more dependent on computer vision and image processing techniques in recent years. Fundus photography, also known as a fundus image, is a method that may be used to capture an image of the back of a persons eye. This article presents optimized deep learning model for diagnostic marking in retinal fundus images towards accurate detection of retinopathy. For experimental work, 500 images were selected from available open source Kaggle data set. 400 images were used to train deep learning model and remaining 100 images were used to validate the model. Images were enhanced using the contrast limited adaptive histogram equalization (CLAHE) algorithm. Pre trained convolutional neural network (CNN) models-AlexNet, VGG16, GoogleNet, and ResNet are used for classification and prediction of images. Accuracy, specificity, precision and F1-score of AlexNet is better than VGG16, ResNet-50, and GoogleNet. Sensitivity of ResNet-50 is higher than other pre trained CNN models.
Accuracy , AlexNet , Deep learning , F1 measure , Fundus image classification , ResNet-50 , Retinopathy detection
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Department of Computer Science and Engineering, R R Institute of Technology, Bangalore, India
G L Bajaj Institute of Technology and Management, Uttar Pradesh, Greater Noida, India
Department of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, India
Department of Artificial Intelligence and Machine Learning, Universal College of Engineering, Near Bhajanlal Dairy & Punyadham, Vasai, India
Department of Computer Engineering, Pravara Rural Engineering College, Maharashtra, India
Department of Engineering and Technology, Gurugram University, Gurugram, India
Al-Farabi Kazakh National University, International Information Technology University, Almaty, Kazakhstan
Department of Computer Science Engineering, Mahakal Institute of Technology, Ujjain, India
Department of Computer Science and Engineering
G L Bajaj Institute of Technology and Management
Department of Electronics and Telecommunications Engineering
Department of Artificial Intelligence and Machine Learning
Department of Computer Engineering
Department of Engineering and Technology
Al-Farabi Kazakh National University
Department of Computer Science Engineering
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