Blood pressure detection using deep convolution neural network models: Xception and InceptionV4

Nida Nasir; Feras Barneih; Omar Alshaltone; Mohammad AlShabi; Ahmed Al Shammaa

doi:10.1117/12.2663999

14 June 2023 Blood pressure detection using deep convolution neural network models: Xception and InceptionV4

Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, Ahmed Al Shammaa

Proceedings Volume 12548, Smart Biomedical and Physiological Sensor Technology XX; 125480I (2023) https://doi.org/10.1117/12.2663999
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Artificial intelligence (AI) i n h ealthcare i s a constantly evolving field that must be explored. Be cause of its practicality and usefulness in estimating various ailments, focused research on AI, specifically deep l earning, is dominating. High blood pressure (BP), also known as hypertension, is a serious health condition. It causes serious issues such as heart attacks, strokes, and even death. As a result, blood pressure should be constantly monitored. The proposed study uses famous CNN models for blood pressure detection and states the results of two main CNN models. Inception-V4 and Xception achieved an accuracy of 96% and 98.8%, respectively. Other performance metrics have been calculated and discussed.This study demonstrates the effectiveness of using deep learning techniques to aid in the diagnosis and prediction of hypertension.

Citation Download Citation

Nida Nasir, Feras Barneih, Omar Alshaltone, Mohammad AlShabi, and Ahmed Al Shammaa "Blood pressure detection using deep convolution neural network models: Xception and InceptionV4", Proc. SPIE 12548, Smart Biomedical and Physiological Sensor Technology XX, 125480I (14 June 2023); https://doi.org/10.1117/12.2663999

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