AI-DRIVEN DIAGNOSTIC TOOL FOR EYE DISEASES: ENHANCING EARLY DETECTION IN REMOTE AREAS THROUGH PORTABLE RETINAL IMAGING
Keywords:
Medical Imaging, Machine Learning, Convolutional Neural Networks, Ophthalmology, BioengineeringAbstract
Recent developments in artificial intelligence (AI) and machine learning (ML) have revolutionized medical diagnostics, offering opportunities for the improvement of healthcare delivery, particularly in remote and underserved communities. This paper introduces PAIRE (Portable Artificial Intelligence based Retinal Imaging), a diagnostic tool designed to analyze frontal-view retinal images captured by accessible devices such as smartphones. PAIRE focuses on early detection of common eye diseases such as Age related Macular Degeneration and Ocular Hypertension, facilitating timely medical intervention in rural communities that lack specialized eye care. Convolutional Neural Networks (CNNs), Feed Forward Neural Networks (FNNs) and transfer learning are used combined with many other techniques. The model is trained on diverse retinal image datasets to ensure robust performance. The tool is evaluated on its accuracy and practicality in real world settings.
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Copyright (c) 2024 Umar Ghani, Aarav Yadav (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
