Article: Optimised VGH algorithm-based deep CNN classifier for diabetic retinopathy Journal: International Journal of Biometrics (IJBM) 2026 Vol.18 No.4 pp.331 - 361 Abstract: High consequential condition of diabetic retinopathy (DR) has an impact on diabetic patients, inaccurate detection results in permanent vision loss. Fundus images are used to predict the severity of DR from the lesions segmentation which takes more time and is a complex process for manual segmentation. Therefore, the proposed research developed the <i>vision guided horse optimiser</i> (VGH algorithm) based on the deep convolution neural network (DCNN) classifier to identify DR. The significance of the present research is based on the vision guided horse-optimised deep convolution neural network (VGH-optimised DCNN) model for classifying DR. Image contrast enhancement, as well as illumination correction, is utilised in the stage of pre-processing. While the automatic Otsu approach as well as the contour-based threshold approach is used to segment both optic disc as well as blood vessel. Various methods are compared with the VGH-optimised DCNN to enhance the model performance. Thus, the accuracy, F1-score, sensitivity, as well as specificity of the developed model by varying epoch25 is 97.59%, 96.76%, 96.64%, and 96.42% at the training percentage of 90, whereas, at the k-fold value 6, the VGH-optimised DCNN model attains the values of 97.10%, 96.77%, 96.22%,and 97.07% utilising the IDRID dataset. Inderscience Publishers - linking academia, business and industry through research

Title: Optimised VGH algorithm-based deep CNN classifier for diabetic retinopathy

Authors: Bhagyashree Somnath Madan; Avinash Sharma

Addresses: Department of Computer Science and Engineering, Madhyanchal Professional University, Bhopal, Ratibad, Bhopal-462044, MP, India ' Department of Computer Science and Engineering, Madhyanchal Professional University, Bhopal, Ratibad, Bhopal-462044, MP, India

Abstract: High consequential condition of diabetic retinopathy (DR) has an impact on diabetic patients, inaccurate detection results in permanent vision loss. Fundus images are used to predict the severity of DR from the lesions segmentation which takes more time and is a complex process for manual segmentation. Therefore, the proposed research developed the vision guided horse optimiser (VGH algorithm) based on the deep convolution neural network (DCNN) classifier to identify DR. The significance of the present research is based on the vision guided horse-optimised deep convolution neural network (VGH-optimised DCNN) model for classifying DR. Image contrast enhancement, as well as illumination correction, is utilised in the stage of pre-processing. While the automatic Otsu approach as well as the contour-based threshold approach is used to segment both optic disc as well as blood vessel. Various methods are compared with the VGH-optimised DCNN to enhance the model performance. Thus, the accuracy, F1-score, sensitivity, as well as specificity of the developed model by varying epoch25 is 97.59%, 96.76%, 96.64%, and 96.42% at the training percentage of 90, whereas, at the k-fold value 6, the VGH-optimised DCNN model attains the values of 97.10%, 96.77%, 96.22%,and 97.07% utilising the IDRID dataset.

Keywords: diabetic retinopathy; optic disc segmentation; blood vessel segmentation; deep CNN; vision guided horse optimiser.

DOI: 10.1504/IJBM.2026.154561

International Journal of Biometrics, 2026 Vol.18 No.4, pp.331 - 361

Received: 05 Apr 2024
Accepted: 20 Jun 2024
Published online: 06 Jul 2026 *

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Title: Optimised VGH algorithm-based deep CNN classifier for diabetic retinopathy

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