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Toward Efficient Identification of Retinal Diseases: A Lightweight Convolutional Neural Network-Based Approach Using
Utsab Saha1, Puja Saha1, Md Jahin Alam2
1Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh.
Healthcare Technology Letters
|February 16, 2026
Summary
A new lightweight deep learning model efficiently detects retinal diseases from optical coherence tomography (OCT) images. This practical approach enhances early diagnosis for better vision preservation.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinal diseases cause significant vision loss, necessitating early detection.
- Optical coherence tomography (OCT) is crucial for diagnosing retinal conditions.
- Existing deep learning models for OCT analysis are often too complex for clinical edge devices.
Purpose of the Study:
- To develop an efficient, lightweight deep learning framework for real-time retinal disease diagnosis.
- To enable practical deployment of AI-powered diagnostic tools on clinical edge devices.
Main Methods:
- Proposed a novel lightweight deep learning architecture for retinal disease detection.
- Integrated a lite convolution block with depthwise separable convolutions for efficiency.
- Incorporated a global-local fusion block and squeeze-and-excitation mechanism for feature capture and refinement.
- Model parameter count is only 0.27 million.
Main Results:
- Achieved high accuracies on benchmark datasets: 99.70% (OCT 2017), 95.00% (OCT C8), and 97.26% (OCTDL).
- Demonstrated strong and stable performance through confusion matrix and ROC analysis.
- Grad-CAM visualizations provided enhanced model interpretability.
Conclusions:
- The proposed lightweight deep learning model offers a practical and efficient solution for real-time retinal disease diagnosis.
- The architecture is suitable for deployment on edge devices, facilitating clinical application.
- The study highlights the potential of efficient AI in improving early detection and management of retinal diseases.
