Abstract:
Spinal fractures are a prevalent type of fracture that have led to serious health issues
which include difficulty in movement and permanent pain. Vertebral fractures are
frequently present in most CT scans taken for abdominal health issues, and so they have
rarely been diagnosed. Moreover, manual detection in medical images is time-consuming
and requires specialized training. Thus, aiming for early and automated vertebral fracture
detection is crucial for effective and fast treatment. Machine learning automated
techniques can be utilized for fracture detection while deep learning models have proven
their power in diagnosing different types of diseases. Specifically, transfer learning
models have proven their effectiveness in diseases’ detection from limited medical data
benchmarks. For this reason, the project suggests the use of transfer learning models to
effectively diagnose vertebral fractures from a CT scan dataset at AUBMC.
Five different deep architectures models (ResNet26, ResNet-RS-50,
Inception_ResNet_v2, Swin_S3_Tiny, and ConvNeXT_Tiny_in22k) have been selected
for investigation to diagnose fracture presence from the AUBMC CT scan dataset after
passing through a series of pre-processing. Segmenting the vertebra along with classical
augmentation were two important pre-processing steps that have improved the binary
classification performance in all models. The results have shown that
ConvNeXT_Tiny_in22k model has attained the highest testing accuracy of 81% without
segmentation and 84.8% with segmentation upon selecting the same number of CT scan
images. The dataset was then extended and segmented where the ConvNeXT model
outperformed with a testing accuracy of 96.4%.
