• Image processing-based automat...

*Result*: Image processing-based automatic tooth segmentation and age estimation in sheep using deep learning.

Title:
Image processing-based automatic tooth segmentation and age estimation in sheep using deep learning.
Authors:
Cihan P; Department of Computer Engineering, Faculty of Corlu Engineering, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye. Electronic address: pkaya@nku.edu.tr., Yıldız A; Department of Computer Engineering, Faculty of Corlu Engineering, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye., Baysan A; Department of Computer Engineering, Faculty of Corlu Engineering, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
Source:
Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft [Ann Anat] 2026 Apr; Vol. 265, pp. 152803. Date of Electronic Publication: 2026 Feb 05.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: G. Fischer Country of Publication: Germany NLM ID: 100963897 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-0402 (Electronic) Linking ISSN: 09409602 NLM ISO Abbreviation: Ann Anat Subsets: MEDLINE
Imprint Name(s):
Original Publication: Jena [Germany] ; New York : G. Fischer, c1992-
MeSH Terms:
Image Processing, Computer-Assisted*/methods , Tooth*/diagnostic imaging , Tooth*/anatomy & histology , Age Determination by Teeth*/methods , Age Determination by Teeth*/veterinary , Deep Learning*, Animals ; Sheep ; Neural Networks, Computer ; Algorithms
Contributed Indexing:
Keywords: Deep learning; Sheep age estimation; Tooth image segmentation; Transfer learning; YOLOv8
Entry Date(s):
Date Created: 20260207 Date Completed: 20260307 Latest Revision: 20260307
Update Code:
20260308
DOI:
10.1016/j.aanat.2026.152803
PMID:
41654275
Database:
MEDLINE

*Further Information*

*Background: Accurate determination of sheep age is essential for optimizing meat quality, reproductive efficiency, feeding strategies, and market value. Conventional age estimation methods rely on subjective visual inspection of dental structures and are prone to inconsistency and human error. Therefore, automated solutions capable of providing objective and reproducible assessments are needed.
Methods: This study proposes a fully automated deep learning and image processing framework for sheep age estimation using dental images. Class imbalance in the dataset was addressed through systematic data augmentation. The images were then segmented using the You Only Look Once version 8 (YOLOv8) algorithm to isolate anatomically relevant tooth regions. Several Convolutional Neural Network (CNN) architectures were evaluated and compared, including VGG16, ResNet50, EfficientNetB0, MobileNetV2, and Xception, fine-tuned via transfer learning, as well as a custom BasicCNN model. A graphical user interface (GUI) was also developed and deployed as a publicly accessible, containerized application to provide a practical and user-friendly implementation of the prediction system.
Results: Among the evaluated models, EfficientNetB0 achieved the highest performance, attaining an overall accuracy of 95 %, with 97 % for the 3-12-month and 2-3-year groups, 92 % for the 1-1.5-year group, and 93 % for the 1.5-2-year group. These results demonstrate that combining automatic segmentation with transfer learning substantially improves model generalization and classification accuracy.
Conclusions: The proposed framework offers a robust, automated, and scalable solution for sheep age estimation. By eliminating manual assessment, the system contributes to precision livestock farming and supports informed decision-making in agricultural practice. The integration of deep learning, automatic segmentation, and a user-friendly interface highlights its potential for broader adoption in real-world field applications.
(Copyright © 2026 Elsevier GmbH. All rights reserved.)*

*Declaration of Competing Interest The authors declare that there is no conflict of interest.*