Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.
Objectives: This research aimed to investigate how different image processing techniques influence the performance of a deep learning model in classifying cervical vertebral maturation (CVM) stages. Methods: A dataset of 799 cephalometric radiographs, originally obtained for orthodontic diagnosis, w...
| Publicado en: | Oral Radiology Vol. 42; no. 3; pp. 552 - 558 |
|---|---|
| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194774134&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194774134 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09116028 1P9 jtl: Oral Radiology issn: 09116028 maglogo: N pubinfo: dt: Jul2026 vid: 42 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194774134 190633868 194774134 194774134 10.1007/s11282-025-00887-2 194774134 ppf: 552 ppct: 6 formats: tig: atl: Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation. aug: au: Yamada, Naoki Iida, Yukihiro Tome, Wakako Katsumata, Akitoshi Kitai, Noriyuki affil: https://ror.org/05epcpp46 Department of Orthodontics, School of Dentistry, Asahi University, 1851, Mizuho, 501-0296, Gifu, Japan sug: subj: Image Processing, Computer Assisted Methods Cervical Vertebrae Radiography Cervical Vertebrae Anatomy and Histology Deep Learning Sensitivity and Specificity Human Cephalometry Orthodontics Convolutional Neural Networks Descriptive Statistics Retrospective Design Record Review Radiography, Dental ab: Objectives: This research aimed to investigate how different image processing techniques influence the performance of a deep learning model in classifying cervical vertebral maturation (CVM) stages. Methods: A dataset of 799 cephalometric radiographs, originally obtained for orthodontic diagnosis, was analyzed. For each image, a rectangular section including the second to fourth cervical vertebrae (C2–C4) was extracted and used as the standard image. A version with low signal intensity was created and termed the "low-density image." Additionally, images were labeled by filling the internal areas of C2, C3, and C4 with yellow, generating three labeled variants: standard labeling, low-density labeling, and dual-color labeling. All image types were classified into six CVM stages (CS1–CS6) using a convolutional neural network based on the AlexNet architecture, implemented via Neural Network Console. From the dataset, 641 images were allocated for learning and 158 for testing. Classification accuracy was evaluated across all image types. Results: The average classification accuracies for the standard and low-density images were 46.8% and 48.7%, respectively. In contrast, labeled images performed significantly better, with accuracies of 82.3% (standard labeling), 81.0% (low-density labeling), and 88.0% (dual-color labeling). Conclusions: Among the image variations, the dual-color labeling dataset achieved the highest classification performance. These results suggest that enhanced labeling techniques can significantly improve the reliability of deep learning models for automated assessment of skeletal maturity. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|