Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/51127
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Taşsoker, Melek | - |
dc.contributor.author | Öziç, Muhammet Usame | - |
dc.contributor.author | Yüce, Fatma | - |
dc.date.accessioned | 2023-06-13T19:10:09Z | - |
dc.date.available | 2023-06-13T19:10:09Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 0250-832X | - |
dc.identifier.issn | 1476-542X | - |
dc.identifier.uri | https://doi.org/10.1259/dmfr.20220108 | - |
dc.identifier.uri | https://hdl.handle.net/11499/51127 | - |
dc.description.abstract | Objectives: The aim of the present study was to compare five convolutional neural networks for predicting osteoporosis based on mandibular cortical index (MCI) on panoramic radiographs. Methods: Panoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on the MCI. The data of the present study were reviewed in different categories including (C1, C2, C3), (C1, C2), (C1, C3), and (C1, (C2 +C3)) as two-class and three-class predictions. The data were separated randomly as 20% test data, and the remaining data were used for training and validation with fivefold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep-learning models were trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC, and training duration. The Gradient-Weighted Class Activation Mapping (Grad-CAM) method was applied for visual interpretation of where deep-learning algorithms gather the feature from image regions. Results: The dataset (C1, C2, C3) has an accuracy rate of 81.14% with AlexNET; the dataset (C1, C2) has an accuracy rate of 88.94% with GoogleNET; the dataset (C1, C3) has an accuracy rate of 98.56% with AlexNET; and the dataset (C1,(C2+C3)) has an accuracy rate of 92.79% with GoogleNET. Conclusion: The highest accuracy was obtained in the differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results. | en_US |
dc.language.iso | en | en_US |
dc.publisher | British Inst Radiology | en_US |
dc.relation.ispartof | Dentomaxillofacial Radiology | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Osteoporosis | en_US |
dc.subject | Panoramic Radiography | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Diagnosis | en_US |
dc.subject | Bone | en_US |
dc.title | Comparison of five convolutional neural networks for predicting osteoporosis based on mandibular cortical index on panoramic radiographs | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 51 | en_US |
dc.identifier.issue | 6 | en_US |
dc.department | Pamukkale University | en_US |
dc.identifier.doi | 10.1259/dmfr.20220108 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57194135354 | - |
dc.authorscopusid | 56246508200 | - |
dc.authorscopusid | 57867839600 | - |
dc.identifier.pmid | 35762349 | en_US |
dc.identifier.scopus | 2-s2.0-85137009015 | en_US |
dc.identifier.wos | WOS:000965801300009 | en_US |
dc.institutionauthor | … | - |
dc.identifier.scopusquality | Q1 | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | 20.03. Biomedical Engineering | - |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection Teknoloji Fakültesi Koleksiyonu WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
16
checked on Nov 16, 2024
WEB OF SCIENCETM
Citations
13
checked on Nov 21, 2024
Page view(s)
40
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.