Image classification using deep learning algorithm for thyroid imaging
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, K.G. | - |
dc.date.available | 2020-02-27T12:43:38Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018-05 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4365 | - |
dc.description.abstract | We conduct image differentiation between benignancy and malignancy for ultrasonography image of thyroid, and also classification of false positive reduction from true positive mass of mammogram images, via convolutional neural networks. For thyroid images we have differentiation accuracy over 76%. For mammogram image classification, we obtained over 80% of accuracy for test datasets. We present the numerical result and corresponding convolutional neural network(CNN) architectures. © 2018 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 | - |
dc.title | Image classification using deep learning algorithm for thyroid imaging | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/IWAIT.2018.8369743 | - |
dc.identifier.bibliographicCitation | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85048794682 | - |
dc.citation.title | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 | - |
dc.contributor.affiliatedAuthor | Kim, K.G. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | computer aided diagnosis | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Thyroid imaging | - |
dc.subject.keywordAuthor | Ultrasonography | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordPlus | Computer aided diagnosis | - |
dc.subject.keywordPlus | Computer aided instruction | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Learning algorithms | - |
dc.subject.keywordPlus | Mammography | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Ultrasonography | - |
dc.subject.keywordPlus | X ray screens | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Convolutional Neural Networks (CNN) | - |
dc.subject.keywordPlus | False-positive reduction | - |
dc.subject.keywordPlus | Mammogram images | - |
dc.subject.keywordPlus | Numerical results | - |
dc.subject.keywordPlus | True positive | - |
dc.subject.keywordPlus | Image classification | - |
dc.description.journalRegisteredClass | scopus | - |
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