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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
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Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Related Experiment Video

Updated: Jul 26, 2026

Three Different Protocols of Corneal Collagen Crosslinking in Keratoconus: Conventional, Accelerated and Iontophoresis
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An optimized multi-scale dilated attention layer for keratoconus disease classification.

K Balaji1, N Gobalakrishnan2

  • 1Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India. kbalajims28@gmail.com.

International Ophthalmology
|July 30, 2025
PubMed
Summary

This study introduces Optimized MSDALNet, a deep learning model for accurate keratoconus (KCN) detection using corneal topography. The model achieves high performance and computational efficiency, offering a promising automated diagnostic tool.

Keywords:
Arctic puffin optimizationExplainable AIKeratoconusMulti-scale dilated attention layer

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Keratoconus (KCN) is a progressive corneal disorder causing visual impairment.
  • Early detection is vital for managing KCN progression.
  • Current diagnostic methods are often time-consuming and subjective.

Purpose of the Study:

  • To develop an automated deep learning (DL) model for keratoconus detection.
  • To enhance diagnostic accuracy and efficiency using corneal topography images.
  • To ensure model interpretability through Explainable AI (XAI).

Main Methods:

  • Developed Optimized MSDALNet with a Multi-Scale Dilated Attention Layer (MSDAL).
  • Optimized model training using Arctic Puffin Optimization (APO).
  • Utilized a public dataset of over 1,100 corneal topography images; employed Grad-CAM for XAI.

Main Results:

  • Achieved high classification performance: 99.5% accuracy, 99.4% precision, 98.4% specificity.
  • Outperformed existing models (CNN, ViT, Swin Transformer) in accuracy and speed.
  • Demonstrated computational efficiency (1.2 GFLOPs) and rapid inference (8.4 ms/image).

Conclusions:

  • Optimized MSDALNet with APO provides an effective, interpretable, and efficient solution for KCN detection.
  • The model shows strong feature extraction capabilities and clinical transparency.
  • Future work aims to improve generalizability with larger, multimodal datasets.