Diabetic Retinopathy Detection From Eye Fundus Images Using Convolutional Neural Network
S. Gayathri Devi, B. Nuthan Prasad, B. Vinay Kumar, V. Vinod
computer-aided diagnosis; convolutional neural networks; deep learning; diabetic retinopathy; diabetic retinopathy classification; diabetic retinopathy lesions localization
Automated Diabetic Retinopathy (DR) detection, screening and diagnosis are critical to save vision loss of patients and assist the ophthalmologists in mass screening. DR screening aims at early treatment of the disease by detecting it before the stage progresses. Present DR analysis systems use digital fundus images for diagnosis reducing the high cost of manual computation. Researchers are continuously persisting for automated screening systems which can reduce the subjective interpretation for ophthalmologists. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. CNN model proposed in this paper provides an accuracy of 87.5% with cross entropy loss of 0.6370 with processing time of 1 minute and 23 seconds. Maximum accuracy improvement of 13% is achieved by the proposed approach over state of the art methods demonstrating preeminence in fundus image classification.
Article Details
Unique Paper ID: 155333

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 797 - 800
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