Classification of chest radiography from general radiography using deep learning approach
Issued Date
2022-01-01
Resource Type
eISSN
24059595
Scopus ID
2-s2.0-85135355771
Journal Title
ICT Express
Rights Holder(s)
SCOPUS
Bibliographic Citation
ICT Express (2022)
Suggested Citation
Tongdee T., Kusakunniran W., Siriapisith T., Saiviroonporn P., Imaromkul T., Yodprom P. Classification of chest radiography from general radiography using deep learning approach. ICT Express (2022). doi:10.1016/j.icte.2022.07.007 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84383
Title
Classification of chest radiography from general radiography using deep learning approach
Author's Affiliation
Other Contributor(s)
Abstract
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%.