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Diabetic retinopathy screening using improved support vector domain description: a clinical study

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Abstract

Diabetic retinopathy (DR) is the major cause of visual impairment among diabetic patients. Significant works have been done to hybrid a modified CNN architecture such as AlexNet with some of classifiers such as support vector machines (SVMs) or fuzzy C-Means (FCM) to improve the DR screening. This new hybrid innovative structure uses more efficient extracting features of a retinal images in both spatial and spectral domains. In spite the advantages of this innovative architecture, the different kernel functions affect the performance of the proposed algorithm. Using the appropriate transformed data into two- or three-dimensional feature maps and using an improved support vector domain description (ISVDD) can obtain more flexible and more accurate image description. To this end, the optimal degree values of different kernel functions can be extracted by using a particle swarm optimization (PSO) algorithm. Also, we compared the performance of our approach (modified-AlexNet-ISVDD) with the results obtained by hybrid modified AlexNet and some of classifiers such as K-Nearest Neighbors (KNN) and FCM clustering. We achieve the proposed CNN architecture using ISVDD on the DIARETDB1 and MESSIDOR datasets, with more than 99% sensitivity.

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Enquiries about data availability should be directed to the authors.

Notes

  1. https://github.com/fchollet/deep-learning-models.

  2. https://www.kaggle.com/diabeticretinopathy-detection.

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Acknowledgements

The author would like to thank Dr. M. Ansari and Khatam-al-Anbia eye hospital employees for making available the data sets used in this paper. The author would also like to thank Dr. Hamid Khakshur and Navid-Didegan Clinic employees for their participation in preparing and labeling the retinal images to use in this study. This work was supported by [Iranian Society of Ophthalmology] (Grant number [ISO-G341397101]) and by [Khorasan Institute of Higher Education] (Grant number [KIHE-13971230]).

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  1. Khorasan Institute of Higher Education, Moalem 77, Mashha, Iran

    Ali Karsaz

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Karsaz, A. Diabetic retinopathy screening using improved support vector domain description: a clinical study. Soft Comput 26, 10085–10101 (2022). https://doi.org/10.1007/s00500-022-07387-z

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