Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge Devices
Diabetic Retinopathy; Edge Computing; IoT (Internet of Things); Machine Learning; Remote Healthcare
This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. 2023 IEEE.
Deshmukh R.; Shalini S.; Vani V.D.; Kn N.M.; Alzubaidi L.H.; Raj V.H.
International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023
Institute of Electrical and Electronics Engineers Inc.
Deshmukh R., School of Engineering, Sr University, Telangana, Warangal, India; Shalini S., J.N.N Institute of Engineering, Department of Mathematics, India; Vani V.D., Institute of Aeronautical Engineering, Hyderabad, Dundigal, India; Kn N.M., Christ (Deemed to Be University), Department of Civil Engineering, Karnataka, Bengalore, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Raj V.H., New Horizon College of Engineering, Department of Applied Sciences, Bangalore, India