Akademik Veri Yönetim Sistemi
Journal of Turkish Spinal Surgery, cilt.35, sa.2, ss.49-54, 2024 (Scopus)
Objective: This study aimed to use deep learning techniques to discriminate different degrees of scoliosis on plain radiographs. Materials and Methods: The study was performed on 1006 standing plain abdominal and chest radiographs (age range 10-18 years) obtained from the archive of our institution. The radiographs were divided into three groups according to the degree of scoliosis: normal (0-9°), mild (10-29°), and moderate/advanced (30° and above). The data were randomly selected and 15% were used for testing, 15% for validation, and the remaining 70% for training. Due to the limited data, the transfer learning (TL) method was used. Pre-trained VGG-16, ResNet-101, and GoogLeNet networks were used for TL. The original classifier was replaced with a new one. Geometric transformations of the radiographs were used for data augmentation. Rotation (-30, 30 degrees), translation (-30, 30 pixels), and scaling (0.9, 1.1 pixels) were applied to the images. The performance of the networks was evaluated using the performance parameters of accuracy, sensitivity, specificity, precision, and F1 score. Results: Overall accuracy after testing the models was determined to be 90.1% for VGG-16, 86.1% for ResNet-101, and 85.5% for GoogLeNet. The accuracy, sensitivity, specificity, precision, and F1 score were 90.1%, 90.7%, 95.0%, 89.9%, and 90.1% for VGG-16, respectively. The VGG-16 values were determined to be higher than those of the ResNet-101 and GoogLeNet networks. Conclusion: The results showed favorable results for deep TL methods in the assessment of normal, mild, and moderate/advanced scoliosis.