Diabetes detection through retinal images utilizing artificial intelligence

Ammar Asad, Zeinab Khadra, Mohammed Hammoud, Ola Haydar, Sergey Lupin

Abstract


Diabetes is a prevalent chronic condition impacting millions globally, with complications such as diabetic retinopathy posing substantial risks, including vision loss. This article critically examines the transformative role of artificial intelligence (AI) in the early detection and diagnosis of diabetic retinopathy through retinal image analysis. Leveraging advanced deep learning algorithms, AI systems can analyze high-resolution retinal images to identify subtle pathological changes often overlooked by human observers. By integrating extensive datasets and employing sophisticated neural networks, these technologies enhance diagnostic accuracy and enable timely interventions, thereby mitigating visual impairment among diabetics. Additionally, we address challenges related to data diversity, ethical concerns, and the necessity for clinical validation of AI tools. As we advance into a new era of medical diagnostics, the collaboration between AI and ophthalmology is revolutionizing patient care, making early detection standard in managing diabetes complications. This study utilizes three widely recognized retina datasets—CHASE_DB1, DRIVE—and employs the U-Net model to develop our neural network while discussing the results.

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