Abstract:
Medical imaging in the form of X-ray radiography is the foundation of diagnosing
musculoskeletal disorders. However, manual interpretation of radiologists is
time-consuming and prone to inconsistency, so automated deep learning-based
classification is essential. This paper uses convolutional neural networks (CNNs)
for classifying musculoskeletal radiographs from the MURA dataset with 5,107
X-ray images. Four deep learning models VGG19, Xception, MobileNetV2, and
ConvNeXtBase were trained and tested using benchmark classification
performance measures: Recall, Precision, and F1-score. MobileNetV2 proved to
be the best baseline with an F1-score of 0.92, which on the application of
hyperparameter tuning with increased learning rate, dropout, and L2
regularization rose to 0.94. Preprocessing techniques involving auto-orientation,
resizing, and data augmentation (blurring, rotation, shearing, and noise
addition) enhanced generalizability in the model. To enhance clinical validity,
interpretability methods Local Interpretable Model-Agnostic Explanations
(LIME), Saliency Maps, and Gradient-weighted Class Activation Mapping
(Grad-CAM) were integrated to achieve visual representation of the model's
focus on clinically important anatomical structures. These methods verified the
model's focus on joint and bone regions, in line with radiology expertise. Fine-
tuned MobileNetV2 model integrated with interpretability offers high accuracy
and computational efficiency, making it clinically suitable for assisting
radiologists. This approach enhances diagnostic precision, reduces workload,
and advances AI-driven medical image analysis for the detection of
musculoskeletal disorders.
