MCRNET-RS: Multi-Class Retinal Disease Classification using Deep Learning-based Residual Network-Rescaled - N, Mohana Suganthi; M, Arun (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2025)
Retinal diseases are a major cause of vision impairment, leading to partial or complete blindness if undiagnosed. Early detection and accurate classification of these conditions are crucial for effective treatment and vision preservation. However, Conventional diagnostic techniques are time-consuming and require professional assistance. Additionally, existing deep-learning models struggle with feature extraction and classification accuracy because of differences in image quality and disease severity. To overcome these challenges, a novel deep learning (DL)-based MCRNET-RS approach is proposed for multi-class retinal disease classification using fundus images. The gathered fundus images are pre-processed using the Savitzky-Golay Filter (SGF) to enhance and preserve essential structural details. The DL-based Residual Network-Rescaled (ResNet-RS) is used to extract hierarchical feature extraction for accurate retinal disease classification. Multi-layer perceptron (MLP) is used to classify retinal diseases such as Diabetic Neuropathy (DN), Branch Retinal Vein Occlusion (BRVO), Diabetic Retinopathy (DR), Healthy, Macular Hole (MH), Myopia (MYA), Optic Disc Cupping (ODC), Age-Related Macular Degeneration (ARMD), Optic Disc Pit (ODP), and Tilted Superior Lateral Nerve (TSLN). The effectiveness of the proposed MCRNET-RS method was assessed using precision, recall, specificity, F1 score, and accuracy. The proposed MCRNET-RS approach achieves an overall accuracy of 98.17%, F1 score of 95.99% for Retinal disease classification. The proposed approach improved the total accuracy by 3.27%, 4.48%, and 4.28% compared to EyeDeep-Net, Two I/P VGG16, and IDL-MRDD, respectively. These results confirm that the proposed MCRNET-RS framework provides a strong, scalable, and highly accurate solution for automated retinal disease classification, thereby supporting early diagnosis and effective clinical decision-making.