基于度量学习的医疗保健异构深度神经网络

N. Poonguzhali, Kagne Raveena Rajendra, T. Mageswari, T. Pavithra
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引用次数: 7

摘要

当大脑内形成异常细胞时,就会发生脑瘤。肿瘤主要有两种类型:恶性肿瘤和良性肿瘤。因此,为了对肿瘤细胞进行早期精确的检测,传统的方法中有各种各样的算法,虽然不能预测出准确的结果,但有助于对肿瘤细胞进行诊断。本文提出了一种可靠的检测方法,利用张量流库、Faster R-CNN算法和SVM分类器来预测患者发生脑相关肿瘤的可能性。更快的R-CNN算法是一种功能强大的分类算法,它的区域提议生成和反对任务都是由同一个卷积网络完成的。
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Heterogeneous Deep Neural Network for Healthcare Using Metric Learning
Brain Tumor occurs when abnormal cells form within the brain. There are two main types of tumors malignant and benign tumors. So for early precise detection of tumor cells, in conventional methods there are various algorithm which helps to diagnosis the tumor cells though it fails to predict an accurate results. This paper presents a reliable detection method by making use of tensor flow library, Faster R-CNN algorithm and SVM classifier used to predict the likely chances of brain related tumor of the patient. Faster R-CNN algorithm is a capable classification algorithm in which both region proposal generation and objection tasks are all done by the same convolutional networks.
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