Akademik Veri Yönetim Sistemi
TRAITEMENT DU SIGNAL, sa.4, ss.2123-2133, 2024 (SCI-Expanded, Scopus)
Non-traumatic vertebral compression fractures are increasingly common due to longer life expectancies. Age-related bone mass loss significantly contributes to these fractures. Typically asymptomatic for extended periods, early detection of non-traumatic vertebral compression fractures can reduce associated health issues and enable more effective treatment. Deep learning methods have shown high accuracy and sensitivity in detecting, classifying, diagnosing, and segmenting various pathological conditions in healthcare. Recently, these methods have been applied more frequently in the detection of non-traumatic vertebral compression fractures and vertebral body segmentation research. This study introduces a unique dataset to apply deep learning techniques, using raw computed tomography (CT) images of patients. The dataset was compiled from retrospective CT images taken at Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology. It includes 197 individuals, with 100 diagnosed with non- traumatic vertebral compression fractures and 97 without. Radiological diagnoses of non- traumatic vertebral compression fractures were added based on CT reports. The dataset comprises a total of 118,200 cross-sectional images in DICOM format, which were enhanced using the Wiener filter. The U-Net network was used to segment 6,301 vertebrae, achieving a 100% dice overlap index score. Additionally, 593 features of vertebral fractures confirmed by reports were extracted using the radiomics method, and 537 features were selected via the logarithmic lambda method. The convolutional neural network (CNN) classification model was employed, achieving an accuracy of 86.7%. AThe classification results were evaluated through ROC-AUC, loss, and accuracy graphs.