基于深度学习图像处理的肺部造影检测研究

Detection of pulmonary angiography based on deep learning image processing

深度学习算法由于善于处理复杂多变的信号数据,在各个行业的实际生产中得到了广泛应用,且其潜力巨大。在图像处理领域,深度学习中的卷积神经网络解决了诸多机器视觉上的难题。在医学图像处理领域,针对其分辨率低,人眼容易误判等问题,为达到辅助医务工作者看清图像、识别病变区域的目的,从神经网络的结构优化、训练方案和预测能力三个方面进行探讨,训练出了专用的卷积神经网络用来判断病变区域。并在肺部医学图像上得到了准确的判断结果,初步证实了卷积神经网络在医学图像领域的实用性和可靠性。

Deep learning algorithm has been widely used in the actual production of various industries because it is good at dealing with complex and changeable signal data,and its potential is huge.In the field of image processing,the convolutional neural network in deep learning solves many difficult problems in machine vision.In the field of medical image processing,aiming at the problems of low resolution and easy misjudgement of human eyes,in order to help medical workers to see the image clearly and identify the lesion area,the structure optimization,training scheme and prediction ability of neural network are discussed,and a special convolutional neural network is trained to judge the lesion area,and obtain it from the lung medical image.Accurate judgment results are obtained,which preliminarily prove the practicability and reliability of convolution neural network in the field of medical images.