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dc.contributor.authorAlmuhayar, Mawandaen_US
dc.contributor.authorLu, Henry Horng-Shingen_US
dc.contributor.authorIriawan, Nuren_US
dc.date.accessioned2020-10-05T02:00:33Z-
dc.date.available2020-10-05T02:00:33Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-4610-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/155071-
dc.description.abstractDeep learning development nowadays has attracted a lot of attention because of its effectiveness and good performance. The performance of deep learning in medical images analysis already can compete with medical image experts. However, there are experts that still believe deep learning only efficient for the big datasets, because of deep learning performance in small datasets still not satisfying enough. In this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest X-ray images with a relatively small dataset. We classify chest X-ray into a binary classification which is a normal image and image with abnormalities. We built and experimented our model using the public dataset of Shenzen Hospital dataset. We also use a different type of input based on different images preprocessing so that the model can perform accurate classification. Based on the result, pre-trained CheXNet with a newly trained fully connected network on the cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain region inside the images, such as other regions outside the lung region and black colored region outside the body region.en_US
dc.language.isoen_USen_US
dc.subjectchest X-rayen_US
dc.subjectclassificationen_US
dc.subjectabnormalitiesen_US
dc.subjectdeep learningen_US
dc.subjecttransfer learningen_US
dc.subjectpreprocessingen_US
dc.subjectCheXNeten_US
dc.titleClassification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNeten_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 3RD INTERNATIONAL CONFERENCE ON INFORMATICS AND COMPUTATIONAL SCIENCES (ICICOS 2019)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000546174100040en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper