PerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis

• URL: http://arxiv.org/abs/2509.18160v2
• Date: Wed, 24 Sep 2025 13:09:54 GMT
• Title: PerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis
• Authors: Akwasi Asare, Isaac Baffour Senkyire, Emmanuel Freeman, Mary Sagoe, Simon Hilary Ayinedenaba Aluze-Ele, Kelvin Kwao,
• Abstract summary: PerceptronCARE is a teleophthalmology application designed for automated diabetic retinopathy detection using retinal images.<n>The system was developed and evaluated using multiple convolutional neural networks, including ResNet-18, EfficientNet-B0, and SqueezeNet.<n>The final model classifies disease severity with an accuracy of 85.4%, enabling real-time screening in clinical and telemedicine settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic retinopathy is a leading cause of vision loss among adults and a major global health challenge, particularly in underserved regions. This study presents PerceptronCARE, a deep learning-based teleophthalmology application designed for automated diabetic retinopathy detection using retinal images. The system was developed and evaluated using multiple convolutional neural networks, including ResNet-18, EfficientNet-B0, and SqueezeNet, to determine the optimal balance between accuracy and computational efficiency. The final model classifies disease severity with an accuracy of 85.4%, enabling real-time screening in clinical and telemedicine settings. PerceptronCARE integrates cloud-based scalability, secure patient data management, and a multi-user framework, facilitating early diagnosis, improving doctor-patient interactions, and reducing healthcare costs. This study highlights the potential of AI-driven telemedicine solutions in expanding access to diabetic retinopathy screening, particularly in remote and resource-constrained environments.

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