Multi-Class Classification of Lung Diseases Using CNN Models
Multi-Class Classification of Lung Diseases Using CNN Models
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DOI：
10.3390/app11199289
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发表时间：
2021-10-01
影响因子：
2.7
通讯作者：
Choi, Seongjun
中科院分区：
文献类型：
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作者：
Hong, Min;Rim, Beanbonyka;Choi, Seongjun
在这项研究中，我们提出了一种利用卷积神经网络(CNN)学习肺部疾病图像的多类分类方法。作为学习的图像数据，使用了美国国立卫生研究院(NIH)分为正常、肺炎和气胸的数据集，以及包括结核病在内的天安市顺春乡大学医院数据集。为了提高性能，在保持长宽比为1：1的情况下，使用中心裁剪进行预处理。作为EfficientNet B7的噪声学生，使用从ImageNet学习的权值进行微调学习，并使用多间隙结构最大限度地利用每一层的特征。实验的结果是，用国家卫生研究院数据集测量的基准函数的性能在测试模型中最高，准确率为85.32%，使用天安市顺春乡大学医院的数据测量的四类预测平均准确率为96.1%，平均灵敏度为92.2%，平均特异度为97.4%，平均推理时间为0.2 S。
In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.