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A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images

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Abstract

Diabetic retinopathy (DR) is a human eye disease in which the eye’s retina is damaged in diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus images, even though that takes longer to diagnose. Among these, the most challenging task in diagnosing the DR disease is edge detection in retinal fundus images to identify the region of infection and its severity. This paper aims to use the adaptive neural network-based Laplacian of Gaussian (AnLoG) classification algorithm on features extracted from diverse retinal fundus images to improve DR disease diagnostic accuracy and reduce training time. Based on the retinal fundus image in the Messidor dataset, the consequence of the proposed AnLoG classification algorithm for detecting diabetic retinopathy is compared to traditional supervised BPN machine learning algorithms and other contemporary techniques. AnLoG has proved its supremacy in terms of accuracy (97.29%), recall (94.64%), precision (93.13%), and F-Score (93.80%). Simulation results show that the proposed technique performs well compared to the existing approach.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by Qatar National Research Fund under the Grant No. MME03-1226-210042. The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India

    Manjula Devi Ramasamy

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Keerthika Periasamy & Suresh Periasamy

  3. Department of Electrical and Electronics Engineering, Kongu Engineering College, Erode, India

    Suresh Muthusamy

  4. Department of Electrical and Electronics Engineering, Theni Kammavar Sangam College of Technology, Theni, India

    Ponarun Ramamoorthi

  5. Department of Engineering, University of Technology and Applied Sciences, Muscat, Sultanate of Oman

    Gunasekaran Thangavel

  6. Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India

    Sreejith Sekaran

  7. Department of Mechanical and Industrial Engineering, Centre for Advanced Materials, Qatar University, Doha, Qatar

    Kishor Kumar Sadasivuni

  8. Centre for Advanced Materials, Qatar University, Doha, Qatar

    Mithra Geetha

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  1. Manjula Devi Ramasamy
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Correspondence to Suresh Muthusamy.

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Ramasamy, M.D., Periasamy, K., Periasamy, S. et al. A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images. Neural Comput & Applic 36, 3513–3524 (2024). https://doi.org/10.1007/s00521-023-09324-z

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