Comparative Analysis of Deep Learning Architectures for Bone Fracture Classification - Yusan, Mehmet Samet; Butun, Ertan (Institute of Electrical and Electronics Engineers Inc., 2025)
The examination of bone fractures found in Xray images by radiologists is both time-consuming and prone to human error. The use of deep learning models to accurately and quickly diagnose bone fractures can assist doctors in treating patients. In this study, various state-of-the-art deep learning models were applied to the FracAtlas dataset utilizing effective data augmentation techniques to classify bone fractures with high accuracy. The performance of the deep learning models used was compared, and the proposed approach achieved an accuracy of 9 2. 4 7%, demonstrating competitive results with existing studies. © 2025 IEEE.
Kemik Kirigi Siniflandirmasi icin Derin Ogrenme Mimarilerinin Karsilastirmali Analizi
9th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2025 -- 27 June 2025 through 28 June 2025 -- Gaziantep -- 211342
Keywords: Bone Fracture; Computer Vision; Data Augmentation; Deep Learning; DenseNet201; EfficientNetB5; InceptionV3; MobileNetV2; X-Ray Images; Xception