International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Diabetes is a diseases that affect the body’s ability to produce or use insulin, a hormone that regulates blood sugar or glucose levels .Diabetic Retinopathy (DR) is an eye disease in humans with diabetes which may harm the retina of the eye and may cause total visual impairment. Therefore it is critical to detect diabetic retinopathy in the early phase to avoid blindness in humans. Our aim is to detect the presence of diabetic retinopathy by applying Machine learning algorithms. Hence we try and summarize the various models and techniques used along with methodologies used by them and analyze the accuracies and results. It will give us exactness of which algorithm will be appropriate and more accurate for prediction. Machine learning consists of a number of stages to detect retinopathy in the images that includes converting image to suitable input format, various preprocessing techniques. It also includes training a model with a training set and validating with a different testing set. Method proposed in this project is Resnet 152.Berfore applying alorithum retinal images must be Preprocessing, and Feature Extraction. First, the images are preprocessed. They are converted. Proper resizing of image is also done. As the images are heterogeneous they compressed into a suitable size and format. Data set used for this project is taken from Kaggle. The main objective of this work is to build a stable and noise compatible system for detection of diabetic retinopathy.

Keywords: Machine learning, Diabetic Retinopathy, Resnet-152

Downloads: | DOI: 10.17148/IJARCCE.2025.141133