Abstract
Cervical vertebrae fractures pose a significant risk to a patient's health. The accurate diagnosis and prompt treatment need to be provided for effective treatment. Moreover, the automated analysis of the cervical vertebrae fracture is of utmost important, as deep learning models have been widely used and play significant role in identification and classification. In this paper, we propose a novel hybrid transfer learning approach for the identification and classification of fractures in axial CT scan slices of the cervical spine. We utilize the publicly available RSNA (Radiological Society of North America) dataset of annotated cervical vertebrae fractures for our experiments. The CT scan slices undergo preprocessing and analysis to extract features, employing four distinct pre-trained transfer learning models to detect abnormalities in the cervical vertebrae. The top-performing model, Inception-ResNet-v2, is combined with the upsampling component of U-Net to form a hybrid architecture. The hybrid model demonstrates superior performance over traditional deep learning models, achieving an overall accuracy of 98.44% on 2,984 test CT scan slices, which represents a 3.62% improvement over the 95% accuracy of predictions made by radiologists. This study advances clinical decision support systems, equipping medical professionals with a powerful tool for timely intervention and accurate diagnosis of cervical vertebrae fractures, thereby enhancing patient outcomes and healthcare efficiency.