Detection and classification of femoral neck fractures from plain pelvic X-rays using deep learning and machine learning methods Derin öğrenme ve makine öğrenmesi yöntemleriyle düz pelvis röntgenlerinden femur boyun kırıklarının tespiti ve sınıflandırılması

Sevinç H. F., Üreten K., Karadeniz T., GÜLTEKİN G. K.

Ulusal Travma ve Acil Cerrahi Dergisi, cilt.31, sa.8, ss.783-788, 2025 (SCI-Expanded, Scopus, TRDizin) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 8
  • Basım Tarihi: 2025
  • Doi Numarası: 10.14744/tjtes.2025.75806
  • Dergi Adı: Ulusal Travma ve Acil Cerrahi Dergisi
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.783-788
  • Anahtar Kelimeler: Deep learning, feature extraction, femoral neck fractures, machine learning, pre-trained networks
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

BACKGROUND: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. METHODS: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG16, ResNet-50, and MobileNetv2. RESULTS: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen’s kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. CONCLUSION: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.