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Retinal fundus image classification for diabetic retinopathy using SVM predictions

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Abstract

Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR.

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Authors and Affiliations

  1. Electronics & Communication Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

    Minal Hardas, Sumit Mathur, Anand Bhaskar & Mukesh Kalla

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  1. Minal Hardas
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  2. Sumit Mathur
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  3. Anand Bhaskar
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  4. Mukesh Kalla
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Contributions

Conceptualization was done by MH and SM. All the literature reading and data gathering were performed by MH. All the experiments and coding was performed by MH. The formal analysis was performed by MH and SM. Manuscript writing original draft preparation was done by MH. Review and editing was done by MH, SM, AB and MK. Visualization work was carried out by MH and SM.

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Correspondence to Minal Hardas.

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Conflict of interest

Authors M. Hardas, S. Mathur, A. Bhaskar and M. Kalla declare that there has been no conflict of interest.

Consent to participate

This article does not contain any studies with animals or humans performed by any of the authors. Informed consent was taken from all the participants whose fundus images were used as and when required. All the necessary permissions were obtained from Institute Ethical committee and concerned authorities.

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Authors have taken all the necessary consents for publication from participants wherever required.

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All authors consciously assure that the manuscript fulfills the following statements: (1) This material is the authors’ own original work, which has not been previously published elsewhere. (2) The paper is not currently being considered for publication elsewhere. (3) The paper reflects the authors’own research and analysis in a truthful and complete manner. (4) The paper properly credits the meaningful contributions of co-authors and co-researchers. (5) The results are appropriately placed in the context of prior and existing research.

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Hardas, M., Mathur, S., Bhaskar, A. et al. Retinal fundus image classification for diabetic retinopathy using SVM predictions. Phys Eng Sci Med 45, 781–791 (2022). https://doi.org/10.1007/s13246-022-01143-1

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