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An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第15期81卷 20611-20642页

作者： Dayana, A. Mary Emmanuel, W. R. Sam Nesamony Mem Christian Coll Dept Comp Sci Marthandam India Manonmaniam Sundaranar Univ Tirunelveli 627012 Tamil Nadu India Nesamony Mem Christian Coll Dept PG Comp Sci Marthandam India

Diabetic Retinopathy (DR) is one of the long-lasting Diabetic retinal disorders that leads to vision impairment eventually blindness in most of the working-age population. The process of classifying the severity level of DR has been a great challenging task as the lesion features are hard to analyze. The screening process requires an effective detection method to classify the subtle pathologies of the retina. Deep neural architectures play a vital role in diagnosing eye disease and helps ophthalmologists to provide timely treatment. This paper proposes an efficient, optimized deep neural network with chronological tunicate swarm algorithm (CTSA) for classifying the severity of DR. Initially, the retinal images captured through the low-quality fundus photography are preprocessed and then subjected to the segmentation process. First, the optic disc and the blood vasculatures are segmented using a U-Net and sparse Fuzzy C-means-based hybrid entropy model. The lesion area is then detected using the Gabor filter banks, and then the features are extracted. The final classification process takes place using a deep Stacked Autoencoder (SAE) jointly optimized with a bio-inspired tunicate swarm algorithm based on the chronological concept. The presented model achieved an average accuracy, sensitivity, specificity and F1-Score values of 95.9%, 88.07%, 96.80% and 85.26% for the DIARETDB0 database and 95.48%, 93.29%, 91.89% and 90.53% for the DIARETDB1 database. The experimental outcome demonstrates the effectiveness and the robustness of the proposed method in the DR classification task.

关键词： Diabetic retinopathy Deep neural network Classification chronological tunicate swarm algorithm Stacked autoencoder

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