Classification of chest radiography from general radiography using deep learning approach
Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–anterior (PA) x-ray images from other 12 classes. Th...
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th-mahidol.843832023-06-19T00:03:42Z Classification of chest radiography from general radiography using deep learning approach Tongdee T. Mahidol University Computer Science Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–anterior (PA) x-ray images from other 12 classes. The first model is constructed for filtering chest PA from the other classes, before constructing the second model to separate the rest of the 12 classes. This is beneficial to address class-imbalanced and overfitting problems, with assists of class weighting and data augmentation. The proposed method achieves promising performances with precision and recall of 100% and F0.5 of 99%. 2023-06-18T17:03:42Z 2023-06-18T17:03:42Z 2022-01-01 Article ICT Express (2022) 10.1016/j.icte.2022.07.007 24059595 2-s2.0-85135355771 https://repository.li.mahidol.ac.th/handle/123456789/84383 SCOPUS |
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Thailand Thailand |
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Computer Science |
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Computer Science Tongdee T. Classification of chest radiography from general radiography using deep learning approach |
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Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–anterior (PA) x-ray images from other 12 classes. The first model is constructed for filtering chest PA from the other classes, before constructing the second model to separate the rest of the 12 classes. This is beneficial to address class-imbalanced and overfitting problems, with assists of class weighting and data augmentation. The proposed method achieves promising performances with precision and recall of 100% and F0.5 of 99%. |
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Mahidol University |
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Mahidol University Tongdee T. |
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Article |
| author |
Tongdee T. |
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Tongdee T. |
| title |
Classification of chest radiography from general radiography using deep learning approach |
| title_short |
Classification of chest radiography from general radiography using deep learning approach |
| title_full |
Classification of chest radiography from general radiography using deep learning approach |
| title_fullStr |
Classification of chest radiography from general radiography using deep learning approach |
| title_full_unstemmed |
Classification of chest radiography from general radiography using deep learning approach |
| title_sort |
classification of chest radiography from general radiography using deep learning approach |
| publishDate |
2023 |
| url |
https://repository.li.mahidol.ac.th/handle/123456789/84383 |
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1781415425176764416 |