Multi-class cervical spine fracture classification using deep ensemble model based on CT images.

Cervical spine fractures present considerable challenges in both diagnosis and treatment. With the increasing incidence of such injuries and the limitations of conventional diagnostic tools, there is a pressing demand for more precise and effective detection methods. This study proposes a robust Multi-class Classification model for Cervical Spine Fractures (MC-CSF) using Computed Tomography (CT) images to enable the precise identification of fracture types. The process of MC-CSF starts with preprocessing input images using an Enhanced Wiener Filtering (EWF) technique to minimize noise while retaining critical structural features. Following this, a Modified Residual Block-assisted ResUNet (MRB-RUNet) model is utilized for segmentation to precisely isolate the cervical spine area. Once segmented, feature extraction combines both deep learning approaches and texture-based analysis, in which deep features are extracted from established models like VGG16 and Residual Network (ResNet), while Local Gabor Transitional Pattern (LGTrP) captures subtle local texture variations. These features are then processed by an ensemble of sophisticated classifiers, including Enhanced LeNet (E-LNet), ShuffleNet, and a deep convolutional neural network (DCNN), each tasked with distinguishing between different fracture types. To enhance overall classification accuracy, a soft voting approach is applied, where the probabilistic outputs of multiple classifiers are aggregated. This strategy leverages the complementary strengths of individual models, resulting in a more robust and reliable prediction of cervical spine fracture categories. The Ensemble model consistently outperforms the traditional approaches with peak accuracy of 0.954, precision of 0.813 and NPV of 0.974, respectively.