• G SURYA
• Dr.V.V.Satyanarayana Tallapragada

Visual deficiency is a major disorder which is caused due to Diabetic retinopathy(DR). This is primarily considered as the main source among the young generation in the recent days. Statistically, it is observed that in a year such deficiency is resulting in loss of vision in 3 out of 4 people who are in the age of 25 to 74. According to the medical sciences, DR advances due to chronic hyperglycemia. DR results in the damage of the microvascular network that exists within the eye, ultimately leading to loss of vision. Microaneurysms (MA) are identified in the patients having DR. This paper proposed a novel framework that grades the level of the ailment in such patients suffering from DR. Initially, morphological techniques are applied for extraction of the MA. Further, these are classified using Deep Convolutional Neural Network (DCNN) are classified and verified with the ground truths. Results show promising results in contrast to the existing techniques. Still, the doctors require further analytic systems that can grade the severity of the ailment. Hence, in this paper it is proposed to apply GLCM over the classified images. The region of occupancy by MA and the area that is occupied by the blood vessels is calculated and graded for further usage. Results show better understanding and grading of the ailment.

Diabetic Retinotpathy, Microaneurysms, Deep Convolutional Neural Network (DCNN), Gray Level Cooccurence matrix (GLCM),ailment.

"DCCN Based Grading of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.659-667, June 2019, Available :http://www.jetir.org/papers/JETIR1907691.pdf

"DCCN Based Grading of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp659-667, June 2019, Available at : http://www.jetir.org/papers/JETIR1907691.pdf