Deep Learning-Based Bone Age Growth Disease Detection (BAGDD) Using RSNA Radiographs

Radiological bone age assessment is essential for diagnosing pediatric growth and developmental disorders. The conventional Greulich-Pyle Atlas, though widely used, is manual, time-intensive, and prone to inter-observer variability. While deep learning methods such as Convolutional Neural Networks (CNNs) offer automation potential, most existing models rely on transfer learning from natural image datasets and lack specialization for medical radiographs. This study aims to address the gap by developing a domain-specific, custom CNN for pediatric bone age prediction. This research proposes a customized CNN architecture trained on the RSNA pediatric bone age dataset, which includes over 12,000 annotated hand X-ray images labeled with age and gender. The pipeline incorporates pre-processing techniques such as image resizing, normalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance input quality. A YOLOv3 object detector is utilized to localize the hand region prior to model training, focusing on the most relevant anatomical structures. Unlike traditional transfer learning models such as ResNet50, VGG19, and InceptionV3, the proposed CNN is tailored for radiographic features using optimized convolutional blocks and domain-aware augmentations. This design improves generalization and reduces overfitting on small or imbalanced subsets. The proposed model achieved a Mean Absolute Error (MAE) of 3.27 months on the test set and 3.08 months on the validation set, outperforming state-of-the-art transfer learning approaches. These results demonstrate the model’s potential for accurate and consistent bone age estimation and highlight its suitability for integration into clinical decision-support systems in pediatric radiology.

@article{Ali2025,
  title     = {Deep Learning-Based Bone Age Growth Disease Detection (BAGDD) Using RSNA Radiographs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Ali and Muhammad Faheem Mushtaq and Saima Noreen Khosa and Naila Kiran and Humaira Arshad and Urooj Akram},
  doi       = {10.14569/IJACSA.2025.0160699},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160699}
}