Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images

Frontiers in Aging Neuroscience

Khin Wee Lai¹  P. Muthu²  Samiappan Dhanalakshmi³  S. Latha³  Azira Khalil⁴ 

[1] Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia;Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai, India;Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India;Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Malaysia;

关键词: carotid artery;    ultrasound image;    machine learning;    deep learning;    stroke;   

DOI  :  10.3389/fnagi.2021.828214

来源: DOAJ

【 摘 要 】

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

【 授权许可】

Unknown