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Updated: Oct 14, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

GLA-Net: A global-local attention network for automatic cataract classification.

Xi Xu¹, Jianqiang Li¹, Yu Guan¹

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Journal of Biomedical Informatics

|November 9, 2021

Summary

This summary is machine-generated.

Early cataract screening using a novel deep learning approach, the global-local attention network (GLA-Net), improves detection and grading accuracy. This method effectively identifies subtle features for better visual impairment prevention.

Keywords:

Automatic cataract classification Deep learning Global–local attention Neural network

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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

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Published on: May 25, 2020

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Cataracts are a leading cause of blindness globally.
Early screening is crucial for preventing vision loss.
Current automated methods often overlook subtle local features in fundus images.

Purpose of the Study:

To develop a deep learning technique for accurate cataract classification.
To address the limitations of existing methods by incorporating local and global features.
To improve early cataract detection and grading using fundus images.

Main Methods:

Introduction of a global-local attention network (GLA-Net).
GLA-Net utilizes two subnets: one for global structure and one for local discriminative features.
Simultaneous learning of multilevel feature representations from fundus images.

Main Results:

GLA-Net achieved high performance: 90.65% detection accuracy, 83.47% grading accuracy.
Achieved 81.11% accuracy for classifying grades 1 and 2 cataracts.
Demonstrated effectiveness on a real clinical dataset.

Conclusions:

The combination of global and local attention is effective for cataract screening.
GLA-Net shows significant potential for improving ophthalmic disease diagnosis.
This approach offers a promising tool for early detection and management of visual impairment.