Multi-Class Classification of Lung Diseases Using CNN Models
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 U...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/19/9289 |
| _version_ | 1797516781709950976 |
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| author | Min Hong Beanbonyka Rim Hongchang Lee Hyeonung Jang Joonho Oh Seongjun Choi |
| author_facet | Min Hong Beanbonyka Rim Hongchang Lee Hyeonung Jang Joonho Oh Seongjun Choi |
| author_sort | Min Hong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:05:41Z |
| format | Article |
| id | doaj.art-5592ace0c3134993b04f050ee74dc9bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:05:41Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5592ace0c3134993b04f050ee74dc9bf2023-11-22T15:50:31ZengMDPI AGApplied Sciences2076-34172021-10-011119928910.3390/app11199289Multi-Class Classification of Lung Diseases Using CNN ModelsMin Hong0Beanbonyka Rim1Hongchang Lee2Hyeonung Jang3Joonho Oh4Seongjun Choi5Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, KoreaHaewootech Co., Ltd., Busan 46742, KoreaHaewootech Co., Ltd., Busan 46742, KoreaHDT, Co., Ltd., Gwangju 61042, KoreaDepartment of Otolaryngology-Head and Neck Surgery, Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan 31151, KoreaIn 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.https://www.mdpi.com/2076-3417/11/19/9289deep learninglung diseasesefficientnetmulti-class classification |
| spellingShingle | Min Hong Beanbonyka Rim Hongchang Lee Hyeonung Jang Joonho Oh Seongjun Choi Multi-Class Classification of Lung Diseases Using CNN Models Applied Sciences deep learning lung diseases efficientnet multi-class classification |
| title | Multi-Class Classification of Lung Diseases Using CNN Models |
| title_full | Multi-Class Classification of Lung Diseases Using CNN Models |
| title_fullStr | Multi-Class Classification of Lung Diseases Using CNN Models |
| title_full_unstemmed | Multi-Class Classification of Lung Diseases Using CNN Models |
| title_short | Multi-Class Classification of Lung Diseases Using CNN Models |
| title_sort | multi class classification of lung diseases using cnn models |
| topic | deep learning lung diseases efficientnet multi-class classification |
| url | https://www.mdpi.com/2076-3417/11/19/9289 |
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