Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application

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dc.authoridALKAN, Ahmet/0000-0003-0857-0764
dc.authoridUlukaya, Sezer/0000-0003-0473-7547;
dc.authorwosidALKAN, Ahmet/AAD-3054-2019
dc.authorwosidSünnetci, Kubilay Muhammed/GZB-0327-2022
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.contributor.authorSunnetci, Kubilay Muhammed
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorAlkan, Ahmet
dc.date.accessioned2024-06-12T10:59:11Z
dc.date.available2024-06-12T10:59:11Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractAs artificial intelligence in medical imaging is used to diagnose many diseases, it can also be employed to diagnose whether a person has periodontal bone loss or not. Accurate and early diagnosis performs a vital task in the treatment of the patient's dental disorder. Therefore, such medical images are known to be an important clinical adjunct. In this manuscript, whether the patient has periodontal bone loss or non-periodontal bone loss is diagnosed employing hybrid artificial intelligence-based systems. Herein, after tagging a total of 1432 images by an expert, we extract 1000 deep image features for each image using AlexNet and SqueezeNet deep learning architectures. On the other hand, we classify these images directly without extracting the image features using the EfficientNetB5 deep learning architecture. First, we categorize AlexNet-based deep image features using the Coarse Tree, Weighted K-Nearest Neighbor (KNN), Gaussian Naive Bayes, RUSBoosted Trees Ensemble, and Linear Support Vector Machine (SVM) classifiers. Afterward, we classify SqueezeNet-based deep image features using Medium Tree, Gaussian Naive Bayes, Boosted Trees Ensemble, Coarse KNN, and Medium Gaussian SVM classifiers. With the help of the ten classifiers employed in this study, we also design a user-friendly Graphical User Interface (GUI) application. Thanks to this application, we aim to reduce the workload of experts, save time and help to diagnose dental disorders early. The results show that the best classifiers for AlexNet-based, SqueezeNet-based, and Direct-Convolutional Neural Network (CNN) are Linear SVM, Medium Gaussian SVM, and EfficientNetB5, respectively. Among these classifiers, the best classifier is Linear SVM, and its accuracy, error, sensitivity, specificity, precision, and F1 score values are 81.49%, 18.51%, 84.57%, 79.14%, 75.68%, and 79.88%, respectively.en_US
dc.identifier.doi10.1016/j.bspc.2022.103844
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85132425350en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103844
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20354
dc.identifier.volume77en_US
dc.identifier.wosWOS:000813959600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectFeature Extractionen_US
dc.subjectGraphical User Interfaceen_US
dc.subjectMachine Learningen_US
dc.subjectPeriodontal Bone Lossen_US
dc.subjectClassificationen_US
dc.subjectImagesen_US
dc.titlePeriodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly applicationen_US
dc.typeArticleen_US

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