胸腔图像中的肺炎分类深度学习方法

Eugenia Arrieta Rodríguez, Agustin Naar, Margarita Gamarra
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引用次数: 0

摘要

我们开发了一种自动学习算法。该算法基于北美放射学会在 "Kaggle "平台上发布的数据集,可识别出是否为肺炎的放射影像。我们从中获得了一组特定的图像,并将其标签按 70% 的比例分割,以训练一个由两个卷积层组成的卷积神经元网络模型,用于提取每个图像中的特征,并在模型训练的分类阶段结束,最后通过使用 30% 的数据集进行测试,得出 74% 的准确度指标。
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A Deep Learning Approach to Classification Pneumonia in Thorax Images
An algorithm of automatic learning was developed. That is abke to identify radiographic images with a pneumonia and not pneumonia diagnose based on a data set published in the “Kaggle” platform by the Radiological Society of North America, from which we obtained a set of specific images with their labels that were divided by 70% to train a convolutional neuronal network model consisting of two convolutional layers for the extraction of characteristics in each image, and ends with a classification stage in the training of the model, to conclude with 74% in metrics of accuracy given by test tests with 30% of the data set.
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