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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.18160 |
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| _version_ | 1866916966600540160 |
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| author | Asare, Akwasi Senkyire, Isaac Baffour Freeman, Emmanuel Sagoe, Mary Aluze-Ele, Simon Hilary Ayinedenaba Kwao, Kelvin |
| author_facet | Asare, Akwasi Senkyire, Isaac Baffour Freeman, Emmanuel Sagoe, Mary Aluze-Ele, Simon Hilary Ayinedenaba Kwao, Kelvin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis Asare, Akwasi Senkyire, Isaac Baffour Freeman, Emmanuel Sagoe, Mary Aluze-Ele, Simon Hilary Ayinedenaba Kwao, Kelvin Computer Vision and Pattern Recognition 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. |
| title | PerceptronCARE: A Deep Learning-Based Intelligent Teleophthalmology Application for Diabetic Retinopathy Diagnosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.18160 |