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Eisoc with ifodpso and dcnn classifier for diabetic retinopathy recognition system

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Abstract

Diabetic patients are tremendously increasing worldwide and it is a chronic disease that may cause complications in Eye, Heart, and Kidneys. Diabetic retinopathy (DR) plays a vital role that causing vision loss in diabetic patients, if not treated at an earlier stage. Nowadays, a lot of patients undergo eye screening per day, therefore ophthalmologists face a lot of challenges during the screening of Diabetic retinopathy. Also, manual screening leads to errors and is more time-consuming, the patients have to wait for much time in the clinic. Hence an automated system is essential to help the ophthalmologist as a secondary opinion in retinal screening. Normally, clinicians will detect the different signs of DR from the retinal images taken through fundus photography, may leads manual error. Here, this research work proposes a novel automated system through the implementation of deep learning techniques in biomedical analysis. This system includes the following stages pre-processing, segmentation, and classification. For analysis, the proposed research article work on retina fundus images is taken from the both Public dataset and the in-house clinical dataset from Chaithanya Eye Hospital Kerala. The first stage is to remove noise from the input image and enhance the contrast of the images. For noise reduction, a Bilateral Filter is utilized first, followed by enhancement utilizing Contrast Limited Adaptive Histogram Equalization with an unsharp technique. Then Thick Blood vessels are segmented from the enhanced image using the Extended Iterative Self-Organizing clustering (EISOC) Method. From the segmented image, GLCM features are extracted and then features are selected using the Improved Fractional-Order Darwinian Particle Swarm Optimization (IFODPSO) technique. Finally, a Deep CNN classifier is used which classifies the image as Diabetic Retinopathy (DR) or Normal case. Using IFODPSO with a DCNN classifier, 96.6% of the predictions are correct, 3.4% are wrong and the parameter such as Sensitivity is 92.5%, Specificity is 98.9%, Precision is 95.9% values are obtained. By way of classifier efficiency estimation, IFODPSO with DCNN classifier is higher than most other standard classifiers in the literature.

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Acknowledgements

The Authors thank the management of the Noorul Islam Center for Higher Education for their continuous support and encouragement. Also, we acknowledge the creator of freely-accessible public Messidor database of diabetic retinopathy. Then we would like to acknowledge Chaithanya Eye hospital Kerala for providing the in-house clinical data. Finally, we would like to thank the anonymous reviewers for helping to organize this text.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    Neetha Merin Thomas

  2. Department of Biomedical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    S. Albert Jerome

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  1. Neetha Merin Thomas
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Contributions

Neetha Merin Thomas1*: Roles: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, Writing-Reviewer Comments Correction, Proof reading and Visualization.

S.Albert Jerome 2: Roles: Visualization, Data Correction, Resources, and Validation Reviewer Comments Correction and Editing.

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Correspondence to Neetha Merin Thomas.

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This study has not been supported by any industrial company and does not serve to promote any commercial product. Anonymized publicly available databases were used in the conducted experiments.

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Thomas, N.M., Jerome, S.A. Eisoc with ifodpso and dcnn classifier for diabetic retinopathy recognition system. Multimed Tools Appl 83, 42561–42583 (2024). https://doi.org/10.1007/s11042-023-17244-2

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