Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
CNNs in Healthcare: Tackling Diabetic Retinopathy

CNNs in Healthcare: Tackling Diabetic Retinopathy

Kumar, Govind
R. Bharamagoudra, Manjula
S. Sadanand, Chethan

Paperback

Medical Reference

ISBN10: 6208422094
ISBN13: 9786208422097
Publisher: LAP Lambert Academic Publishing
Published: Jan 7 2025
Pages: 124
Weight: 0.39
Height: 0.29 Width: 6.00 Depth: 9.00
Language: English
Diabetic retinopathy is a leading cause of vision impairment and blindness worldwide, affecting millions annually. Arising from prolonged hyperglycaemia, it damages the retina's blood vessels, leading to retinal ischaemia, microaneurysms, and haemorrhages, which may result in vision loss or blindness. In 2020, 103.12 million adults globally were affected by DR, with projections reaching 160.50 million by 2045. Timely detection is crucial for preventing vision loss and improving quality of life, while also reducing the societal burden of blindness. The rise of deep learning, particularly convolutional neural networks (CNNs), has revolutionized automated DR detection, offering a promising path for early intervention. Technologies such as fundus photography, optical coherence tomography (OCT), and AI-driven screening systems complement these advancements. This project proposes a Health Monitoring System that uses CNNs to automate DR detection and grading. By incorporating personalized patient data, the system enhances diagnostic accuracy and provides tailored recommendations to patients and doctors. This personalized approach aims to prevent vision loss and improve patient outcome.

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