Deep Learning based ROI Segmentation using Convolution Neural Network

R. Arunadevi, S. Sudha, V. Karthi, M. D. Saranya, Thurai V B Raaj, Kavin Kumar K
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Abstract

Atherosclerosis is a chronic degenerative disease that results in cardiovascular diseases (CVDs) and is detected either by cardiac arrest or stroke. Early diagnosis of CVDs is made possible by identifying Intima Media Thickness (IMT) and elasticity. B-mode ultrasound imaging has on no account ionizing radiation and is economical and non-invasive to assess CVDs. This paper proposes an effective automatic image segmentation method using deep learning CNN for segmenting the region containing intima media of far wall carotid artery. The proposed approach is compared with SVM classifier and RBF neural network and is proven to be robust with improved accuracy and F1 score.
基于卷积神经网络的深度学习ROI分割
动脉粥样硬化是一种导致心血管疾病(cvd)的慢性退行性疾病,可通过心脏骤停或中风来检测。通过确定内膜中膜厚度(IMT)和弹性,可以早期诊断心血管疾病。b超成像没有电离辐射,是一种经济、无创的心血管疾病评估方法。本文提出了一种有效的自动图像分割方法,利用深度学习CNN对远壁颈动脉中膜所在区域进行分割。将该方法与支持向量机分类器和RBF神经网络进行了比较,结果表明该方法具有较好的鲁棒性,具有较高的准确率和F1分数。
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