• Donika Umesh Chaudhari
• Lalit Patil

Retinopathy analysis is necessary by automated system because as world is moving fast in progress with technology, a human illness and defect in visibility of eyes is also growing rapidly. According to project survey for this paper almost 50-52% of world population has problems in their visibility. A method for segmentation of the diabetic retinopathy based of the image. The original image is to be converted to gray scale image. Then adaptive histogram equalization is applied. The fuzzy c-means clustering is applied to segment the blood vessels in the image. Gray Level Covariance Method (GLCM) is used to extract 22 features. Finally, the images are classified by PNN (probabilistic neural network) classification. Algorithm is implemented in MATLAB and tested for various numbers of retinal images.

Retinopathy, Fuzzy C means, Probabilistic neural network, segmentation

"Application Of Fuzzy For Diabetic Retinopathy Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.772-777, June 2019, Available :http://www.jetir.org/papers/JETIR1908270.pdf

"Application Of Fuzzy For Diabetic Retinopathy Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp772-777, June 2019, Available at : http://www.jetir.org/papers/JETIR1908270.pdf