A Transfer Learning-Based Approach to Detect Cerebral Microbleeds
作者机构：Mixed Reality and Interaction LabDepartment of SoftwareSejong UniversitySeoul143-747Korea
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第71卷第4期
页 面：1903-1923页
核心收录：
基 金：This research was supported by the MSIT(Ministry of Science and ICT) Korea under the ITRC(Information Technology Research Center)support program(IITP-2021–2016–0–00312)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)
主 题：Microbleeds deep convolutional neural network ResNet50 AlexNet computer-vision
摘 要：Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum *** and elderly people with brain injury and dementia can have small microbleeds in their brains.A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with *** this study,we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging(SWI)data *** proposed structure comprises two different pretrained convolutional models with four *** stages include(i)skull removal and augmentation,(ii)making clusters of data samples using the k-mean classifier,(iii)reduction of false positives for efficient performance,and(iv)transfer-learning *** proposed technique was assessed using the SWI dataset available for 20 *** our findings,we attained an accuracy of 97.26%with a 1.8%false-positive rate using data augmentation on the AlexNet transfer learning model and a 1.1%false-positive rate with 97.89%accuracy for the ResNet 50 model with data augmentation *** results show that our models outperformed the existing approach for the detection of microbleeds.
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