Advancing Diabetic Retinopathy Detection and Severity Classification using Dynamic SwishNet-181

Authors

  • K. Kayathri Ph.D Research Scholar, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, TN, India
  • K. Kavitha Assistant Professor, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India

Keywords:

Diabetic Retinopathy Detection, Dynamic SwishNet-181, Image Preprocessing Techniques, Deep Learning Evaluation Metrics, Vision Impairment Prevention

Abstract

Timely detection of Diabetic Retinopathy (DR) is critical in preventing vision impairment among diabetic individuals. This research introduces Dynamic SwishNet-181, a novel neural network architecture tailored for classifying DR severity levels (ranging from 0 for No DR to 4 for Proliferative DR). Unique to this study is the integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Anisotropic Diffusion Filtering (ADF) as preprocessing techniques, refining retinal images by enhancing contrast and reducing noise. The evaluation of Dynamic SwishNet-181 includes a comparison against established CNN models such as VGG16, EfficientNET, and RESNET using performance metrics like accuracy, precision, recall, and F1-score. This comprehensive analysis aims to empower medical professionals by providing a reliable and accurate tool for diagnosing DR efficiently. By merging advanced deep learning models with image enhancement methods, this research offers a promising approach for accessible and dependable DR screening, potentially preventing vision loss in diabetic patients.

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Published

24.03.2024

How to Cite

Kayathri, K. ., & Kavitha, K. . (2024). Advancing Diabetic Retinopathy Detection and Severity Classification using Dynamic SwishNet-181. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 61–77. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5119

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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Copyright (c) 2024 K. Kayathri, K. Kavitha

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