TY - GEN
T1 - Hypertension Detection Using AI and Retinal Image Analysis
AU - Shankar, Srinivasan Balapangu
AU - Richardson Ansah, Margaret
AU - Ameyaw, Robert
AU - Sam, Akyereba Kukua
AU - Jeff Abbu-Bonsra Kyei, John
AU - Amo, Deborah
AU - Okae, Percy
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.
AB - This research paper introduces a method for detecting hypertensive retinopathy using advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) applied to retinal fundus images. By analysing a set of over 5469 images from the UK Biobank we were able to achieve an accuracy rate of 81% in identifying signs of hypertensive retinopathy. Additionally, we improved our model by incorporating the detection of multiple eye conditions such, as diabetes, glaucoma, cataracts and age-related macular degeneration leading to overall diagnostic precision and providing a comprehensive screening tool. To make it easier for healthcare professionals to use this model in practice we developed a user-friendly mobile application that allows them to upload retinal images and receive diagnostic predictions displayed in visual formats like histograms and bar charts. This innovation not only makes the model accessible but also provides intuitive visual feedback, aiding in clinical decision-making.
KW - Artificial Intelligence
KW - Convolutional Neural Networks
KW - Hypertensive Retinopathy
KW - Mobile Application
KW - Multi- Disease Detection
KW - Retinal Fundus Images
UR - http://www.scopus.com/inward/record.url?scp=85217846285&partnerID=8YFLogxK
U2 - 10.1109/ICAST61769.2024.10856490
DO - 10.1109/ICAST61769.2024.10856490
M3 - Conference contribution
AN - SCOPUS:85217846285
T3 - IEEE International Conference on Adaptive Science and Technology, ICAST
BT - Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024
PB - IEEE Computer Society
T2 - 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024
Y2 - 24 October 2024 through 26 October 2024
ER -
