Deep-Learning Classifiers for Small Data Orthopedic Radiology

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.