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Implementation of convolutional neural network categorizers in coronary ischemia detection

机译:冠状动脉缺血检测中卷积神经网络分类的实施

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The heart is one of the most important and sophisticated organ of the human body. Coronary ischemia is a condition in which the coronary muscles do not receive sufficient blood and oxygen because of blocked or tightened heart vessels. This syndrome is called cardiac vessel illness. There have been numerous attempts to detect the impact of cardiac vessel illness on the heart muscles using noninvasive experiments. Most of the effects of ischemia as well as severe cardiac conditions on the muscles of the ventricle parts can be detected using ultrasonic images. If treatment is provided to suspected cases in the early stage of cardiac vessel illness, the chance of survival is high; for this, many software-based detection approaches have been used. In this study, we propose an approach that can automatically diagnose the cardiac artery disease by using the cardiac echo images of the four parts of the heart.
机译:心脏是人体最重要和最复杂的器官之一。冠状动脉缺血是一种冠状动脉肌,因为堵塞或拧紧心脏血管,冠状肌肉不会收到足够的血液和氧气。这种综合症称为心脏血管疾病。许多尝试使用非侵入性实验检测心脏血管疾病对心脏肌肉的影响。使用超声图像可以检测缺血的大多数缺血和严重的心脏条件对心室部件的肌肉的影响。如果在心脏血管疾病的早期阶段提供治疗,则存活的机会很高;为此,已经使用了许多基于软件的检测方法。在这项研究中,我们提出一种方法,一种方法可以通过使用心脏四个部分的心脏回声图像自动诊断心动脉疾病。

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