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Diabetes, hypertension, obesity, glaucoma, etc. are severe and common retinopathy diseases today. Early age detection and diagnosis of these diseases can save human beings from many life threats. The retina's blood vessels carry details of retinopathy diseases. Therefore, feature extraction from blood vessels is essential to classify these diseases. A segmented retinal image is only a vascular tree of blood vessels. Feature extraction is easy and efficient from segmented images. Today, there are existing different approaches in this field that use RGB images only to classify these diseases due to which their performance is relatively low. In the work, we have proposed a model based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma, and multi-class diseases. We have carried out extensive experiments on numerous images of DRIVE, HRF, STARE, and RIM-ONE DL datasets. The highest accuracy of the proposed approach is 90. 90 %, 95. 00 %, and 92. 90 % for diabetic retinopathy, glaucoma, and multi-class diseases, respectively, which the model detected better than most of the methods in this field.