Identification of Diabetic Retinopathy from fundus images using CNNs
Laxmi Math1, Ruksar Fatima2
1Laxmi Math, Department of Computer Science, KBN College of Engineering, Kalaburagi. Karnataka, India.
2Dr. Ruksar Fatima, Department of Computer Science, Vice Principal, KBN College of Engineering, Kalaburagi, Karnataka, India.
Manuscript received on October 17, 2019. | Revised Manuscript received on 23 October, 2019. | Manuscript published on November 10, 2019. | PP: 3439-3443 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4598119119/2019©BEIESP | DOI: 10.35940/ijitee.A4598.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: the Diabetic Retinopathy is the diabetes-mellitus to human vision that is the main cause of vision loss. The early stage detection of diabetic retinopathy is can play eminent role in the diabetes treatment. The fundus of retinal image is utilized to recognize the symptoms of diabetic retinopathy. Moreover, the above phenomena led us to propose this paper; here we propose segment based learning approach for identification of diabetic retinopathy. The segment based image level is required to obtain the identification of diabetic retinopathy images, the classifiers and features are equally learned from the data. Then, we adapt pre-trained CNN as the fine tune to achieve the segment level estimation of diabetic retinopathy. For identification of diabetic retinopathy, we achieve accuracy 96.97 and 98.46% at 20 and 30% and also achieve AUC (Area under Curve) 97.51 and 98.50 at 20 and 30% on the Kaggle dataset. Our proposed model outperforms much better than other models.
Keywords: Diabetic Retinopathy, DL (deep learning), CNN (convolutional neural network).
Scope of the Article: Deep learning