DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Yuyeon | ko |
dc.contributor.author | Kim, Taewan | ko |
dc.contributor.author | Han, Mi-Ryung | ko |
dc.contributor.author | Kim, Sejin | ko |
dc.contributor.author | Kim, Geunyoung | ko |
dc.contributor.author | Lee, Seungchul | ko |
dc.contributor.author | Choi, Youn Jin | ko |
dc.date.accessioned | 2023-09-13T03:00:32Z | - |
dc.date.available | 2023-09-13T03:00:32Z | - |
dc.date.created | 2023-09-13 | - |
dc.date.created | 2023-09-13 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v.12, no.1 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312541 | - |
dc.description.abstract | Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis. We designed a CNN-CAE model that removes the unnecessary information (e.g., calipers and annotations) from ultrasound images and classifies ovaries into five classes. We used fivefold cross-validation to evaluate the performance of the CNN-CAE model in terms of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) was applied to visualize and verify the CNN-CAE model results qualitatively. In classifying normal versus ovarian tumors, the CNN-CAE model showed 97.2% accuracy, 97.2% sensitivity, and 0.9936 AUC with DenseNet121 CNN architecture. In distinguishing malignant ovarian tumors, the CNN-CAE model showed 90.12% accuracy, 86.67% sensitivity, and 0.9406 AUC with DenseNet161 CNN architecture. Grad-CAM showed that the CNN-CAE model recognizes valid texture and morphology features from the ultrasound images and classifies ovarian tumors from these features. CNN-CAE is a feasible diagnostic tool that is capable of robustly classifying ovarian tumors by eliminating marks on ultrasound images. CNN-CAE demonstrates an important application value in clinical conditions. | - |
dc.language | English | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder | - |
dc.type | Article | - |
dc.identifier.wosid | 000866180700012 | - |
dc.identifier.scopusid | 2-s2.0-85139613235 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | SCIENTIFIC REPORTS | - |
dc.identifier.doi | 10.1038/s41598-022-20653-2 | - |
dc.contributor.localauthor | Lee, Seungchul | - |
dc.contributor.nonIdAuthor | Jung, Yuyeon | - |
dc.contributor.nonIdAuthor | Kim, Taewan | - |
dc.contributor.nonIdAuthor | Han, Mi-Ryung | - |
dc.contributor.nonIdAuthor | Kim, Sejin | - |
dc.contributor.nonIdAuthor | Kim, Geunyoung | - |
dc.contributor.nonIdAuthor | Choi, Youn Jin | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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