期刊
APPLIED SCIENCES-BASEL
卷 11, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/app11199289
关键词
deep learning; lung diseases; efficientnet; multi-class classification
类别
资金
- Korea Medical Device Development Fund - Korea government [HW20C2174]
- Soonchunhyang University Research Fund
- National Research Foundation of Korea [NRF-2019K1A3A1A20093097]
- National Research Foundation of Korea [2019K1A3A1A20093097] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study proposed a multi-class classification method using CNN to learn lung disease images, utilizing datasets from NIH and Soonchunhyang University Hospital in Cheonan, achieving high accuracy rates through preprocessing and fine-tuning learning techniques.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据