دورية أكاديمية
Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning
| العنوان: | Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning |
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| المؤلفون: | Sota Kato, Masahiro Oda, Kensaku Mori, Akinobu Shimizu, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kazuhiro Hotta |
| المصدر: | Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-022-24936-6 |
| URL الوصول: | https://doaj.org/article/0e766a2a839741df91152e907121ac6b |
| رقم الأكسشن: | edsdoj.0e766a2a839741df91152e907121ac6b |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-022-24936-6 |