A Deep Learning Framework for Skeletal Maturity Evaluation

DOI

10.2991/978-94-6239-713-2_7How to use a DOI?

Keywords

Bone Age Assessment; Convolutional Neural Network; X-ray Imaging; DICOM; Deep Learning; Django Web Application; Pediatric Growth Analysis

Abstract

The paper proposes a web-based implementation for a bone age classification system that assists clinicians in interpreting skeletal maturation in children using pediatric hand X-ray images. The system is implemented using the Django framework and incorporates a convolutional neural network (CNN) architecture to automatically predict bone age from the input images. The proposed image classification system supports multiple image formats, including DICOM, which contains embedded patient information such as gender, birth date, and study date. These details are automatically extracted to compute the chronological age of the patient. The system processes images using deep learning–based feature extraction and predicts bone age based on learned skeletal patterns. The performance of the proposed model is evaluated using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the CNN-based bone age prediction model achieves an accuracy of 82%, precision of 82%, recall of 79%, and an F1-score of 81%, indicating the effectiveness of the proposed system for automated skeletal maturity assessment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mokkala Kiran Moni
AU  - K. Neelima
AU  - P. Rajat Kumar
AU  - V. Prashanth Kumar
AU  - R. Leelavathi Bai
PY  - 2026
DA  - 2026/06/25
TI  - A Deep Learning Framework for Skeletal Maturity Evaluation
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 100
EP  - 110
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_7
DO  - 10.2991/978-94-6239-713-2_7
ID  - Moni2026
ER  -