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The Retina

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Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning.

Govindharaj I¹, Poongodai A², Gnanajeyaraman Rajaram³

¹Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India.

|January 13, 2025

Summary

This study introduces a hybrid AI model combining UNet++ and Capsule Network (CapsNet) for improved glaucoma detection. The novel approach enhances optic cup and disc segmentation accuracy, outperforming existing methods for earlier diagnosis.

Keywords:

Capsule network Convolutional neural network Diabetic retinopathy detection Hybrid UNet++-CapsNet Framework for Automated Glaucoma Detection Optic cup Optic disc U-shaped network Vision disorder

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Deep Neural Networks for Image-Based Dietary Assessment

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma diagnosis relies on analyzing optic disc and cup features in fundus images.
Current detection methods face challenges due to the complexity of glaucomatous changes.
Artificial intelligence offers promising avenues for enhancing diagnostic accuracy.

Purpose of the Study:

To develop and evaluate a hybrid AI model for accurate glaucoma detection.
To improve optic disc and cup segmentation using deep learning architectures.
To compare the performance of the hybrid model against existing state-of-the-art methods.

Main Methods:

A hybrid deep learning model integrating UNet++ for segmentation and Capsule Network (CapsNet) for classification was developed.
Retinal images were pre-processed using Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE).
The model was trained and validated on benchmark datasets for optic cup/disc segmentation and glaucoma detection.

Main Results:

The hybrid UNet++ and CapsNet model demonstrated superior performance in optic cup and disc segmentation.
Pre-processing techniques significantly enhanced retinal image quality.
The proposed model achieved higher glaucoma detection accuracy compared to conventional and current state-of-the-art approaches.

Conclusions:

The hybrid UNet++ and CapsNet model presents an effective strategy for improving glaucoma diagnosis.
Enhanced image pre-processing and advanced AI architectures contribute to more accurate detection.
This approach holds potential for earlier and more reliable identification of glaucoma, preventing vision loss.