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An abnormality detection of retinal fundus images by deep convolutional neural networks

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Abstract

Identification of retinal diseases is a test for the ophthalmologists as the anomalies are just unmistakable at the beginning period. Early detection of these diseases can avoid lasting vision misfortune. Dealing with a lot of retinal images and location of variations from the norm because of these infections is difficult just as tedious. In this work, the deep learning algorithm has proposed to check the abnormality condition of retina with the help of retinal fundus images. In deep leaning a training set is produced with features of variations from the norm present in the retinal images and the infection the retina is experiencing. The deep Convolutional Neural Network (CNN) classifier predicts the infection for every retinal images in the wake of social event the learning from training the set. The rightness of desire is resolved to evaluate the viability of the classifier. The proposed technique was executed in MATLAB and assessed both normal and abnormal diabetic retinopathy retinal images of IDRID, ROC, and local datasets. The proposed technique has gotten better execution measurements, for example, sensitivity of 98.2%, Specificity of 98.45%, accuracy of 98.56% and average area under receiver operating characteristics of 0.9 when contrasted with different conditions of the workmanship strategies.

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Acknowledgements

The authors would like to acknowledge The Indian Diabetic Retinopathy Image dataset(IDRID), Retinopathy Online Challenge (ROC) dataset for making online and also acknowledge Dr.Parthapratim Roy, Ophthalmologist, department of Ophthalmology, Silchar Medical College and Hospital, Assam, India for provided real time retinal fundus image and valuable guidelines for writing this paper. The authors would like to thank Dr.Tripti Goel, Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam for helping to validate this paper.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    R. Murugan

  2. Department of Ophthalmology, Silchar Medical College and Hospital, Silchar, Assam, 788014, India

    Parthapratim Roy

  3. Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, 302031, India

    Utkarsh Singh

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  1. R. Murugan
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Correspondence to R. Murugan.

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Murugan, R., Roy, P. & Singh, U. An abnormality detection of retinal fundus images by deep convolutional neural networks. Multimed Tools Appl 79, 24949–24967 (2020). https://doi.org/10.1007/s11042-020-09217-6

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