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All Journal IAES International Journal of Robotics and Automation (IJRA)
Natarajan Mohana Suganthi
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Automotive Engineering Electrical & Electronics Engineering

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PIKER-NET: Multi-class retinal disease classification using Pied Kingfisher optimization-based improved residual network Lissy Devasahayam; Ramya Devi Murugadasan; Anandhi Samuel Vijayalakshmi; Chanthiya Puhalenthi; Ramnath Muthusamy; Ahilan Appathurai; Natarajan Mohana Suganthi
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp678-689

Abstract

Retinal diseases are vision-threatening conditions, including age-related macular degeneration (ARMD), diabetic retinopathy (DR), and glaucoma, that require early and accurate detection to prevent blindness. However, existing methods often struggle with limited feature representation, high inter-class similarity, intra-class variability, and reduced performance in handling noisy and low-quality retinal images. To address these challenges, a novel PIKER-NET framework is proposed for accurate multi-class retinal disease classification. The input fundus images from the RFMiD dataset are pre-processed using a scalable range adaptive bilateral (SCRAB) filter to enhance image clarity by preserving edges while reducing noise. The Improved Residual Network-Rescaled (ImResNet-RS) integrated with Temporal Attention is then employed to extract deep hierarchical features with enhanced discriminative power. Pied Kingfisher Optimization (PKO) algorithm is utilized for feature selection, effectively reducing redundant information while retaining the most relevant features. Residual Multilayer Perceptron (ResMLP) is used to classify retinal diseases into ARMD, branch retinal vein occlusion (BRVO), diabetic neuropathy (DN), DR, healthy, and myopia (MYA). The PIKER-NET achieves an overall accuracy of 98.14% and F1-score of 97.06%. The PIKER-NET approach improves overall accuracy by 3.24%, 4.24%, 6.23%, and 2.00% compared to EyeDeep-Net, IDL-MRDD, DeepDiabetic, and VisionDeep-AI, respectively. The proposed approach has strong clinical relevance by supporting earlier disease screening, reducing misdiagnosis, and enabling faster diagnosis to assist ophthalmologists in improving patient outcomes.
Co-Authors Ahilan Appathurai Anandhi Samuel Vijayalakshmi Chanthiya Puhalenthi Lissy Devasahayam Ramnath Muthusamy Ramya Devi Murugadasan