Reduwan, Nor Hidayah and Aziz, Azwatee Abdul Abdul and Razi, Roziana Mohd and Abdullah, Erma Rahayu Mohd Faizal and Nezhad, Seyed Matin Mazloom and Gohain, Meghna and Ibrahim, Norliza (2024) Application of deep learning and feature selection technique on external root resorption identification on CBCT images. BMC Oral Health, 24 (1). p. 252. ISSN 1472-6831, DOI https://doi.org/10.1186/s12903-024-03910-w.
Full text not available from this repository.Abstract
BackgroundArtificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification.MethodsExternal root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance.ResultsRF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs.ConclusionIn general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.
| Item Type: | Article |
|---|---|
| Funders: | Fundamental Research Grant Scheme (FRGS), Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Centre of Oral and Maxillofacial Diagnostic and Medicine Studies, Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh, Malaysia |
| Uncontrolled Keywords: | External root resorption; Cone beam computed tomography; Artificial intelligence; Deep learning; Feature selection technique; Classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RK Dentistry |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Dentistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Nov 2024 01:38 |
| Last Modified: | 06 Nov 2024 01:38 |
| URI: | http://eprints.um.edu.my/id/eprint/45600 |
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