Diabeto Care : smart detection and support for diabetes

Diabetic Retinopathy is a leading cause of preventable blindness, and its early detection is essential for saving vision. Traditional manual diagnosis is time-consuming and prone to human error, highlighting the need for automated solutions. Convolutional Neural Networks (CNNs) provide a powerful deep learning approach for analyzing retinal images, detecting abnormalities, and classifying the severity of DR with high accuracy. The proposed web-based system makes screening faster, more reliable, and accessible, supporting ophthalmologists in clinical decision-making while improving patient care. With further advancements and integration into healthcare systems, CNN-based DR detection can significantly reduce diabetes-related vision loss worldwide

[1]             Smith, J., et al., “Comparative Study of ML Algorithms for Diabetes Detection,” IEEE Access, 2018. [2]             Patil, R., “IoT Enabled Smart Health Monitoring System for Diabetes,” IJET, 2021 [3]             World Health Organization (WHO), “Global Report on Diabetes,” 2022 [4]             A. Sharma, “Deep Learning Approaches in Healthcare Analytics,” Elsevier, 2023. [5]             Bhoi, S.K. Prediction of diabetes in females of pima Indian heritage: A complete supervised learning approach Turk. J. Comput. Math. Educ. (TURCOMAT) 2021, 12, 3074–3084. [6]             Mellitus, D. Diagnosis and classification of diabetes mellitus. Diabetes Care 2005, 28, S5–S10. [7]             Pippitt, K.; Li, M.; Gurgle, H.E. Diabetes mellitus: Screening and diagnosis. Am. Fam. Physician 2016, 93, 103–109. [PubMed]