Aslan, B. and Kazaka, W. and Slaven, T. and Chetty, S. and Kruger, N. and Nitschke, G. (2025) Deep-Learning Classifiers for Small Data Orthopedic Radiology, Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2025), Trondheim, Norway, IEEE Press.
Full text not available from this repository. (Use alternate locations listed below)Abstract
Training deep-learning classifiers in orthopedic pathology is problematic due to the scarceness of extensive datasets for training and testing meaning most orthopedic image data is small, sparse and noisy. This study evaluates the efficacy of various state-of-the-art supervised Convolutional Neural Network (CNN) image classifiers, complemented by data augmentation and transfer-learning, versus various Neural Architecture Search (NAS) based deep-learning classifiers. These classifiers are comparatively evaluated on two (cervical spine and elbow) small, multi-label (with unbalanced data distribution) orthopedic radiographic (X-ray) datasets, with the objective of detecting multiple pathologies with high accuracy. To bypass the pervasive problem of small datasets medical datasets, we implement preprocessing and layer freezing to boost all task performance metrics (accuracy, precision, recall, specificity, F1 score), with the ResNet CNN and EfficientNet classifiers yielding the best results overall. Results highlight the efficacy of applying specially tuned CNN and NAS classifiers to small, unbalanced and noisy datasets indicative of those used in orthopedic radiology, demonstrating the potential of such methods as automated prognostic and diagnostic tools to assist orthopedic practitioners.
| Item Type: | Conference paper |
|---|---|
| Subjects: | Social and professional topics > Computing / technology policy > Medical information policy > Medical technologies Computing methodologies > Machine learning Computer systems organization > Architectures > Other architectures > Neural networks |
| Date Deposited: | 26 Jun 2025 09:00 |
| Last Modified: | 26 Jun 2025 09:00 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1729 |
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