VGG16 Transfer Learning for Bone Fracture Classification Using X-Ray Images
DOI:
https://doi.org/10.30871/jaic.v10i3.12739Keywords:
Bone Fracture, X-Ray, Deep Learning, Transfer Learning, VGG16Abstract
Bone fracture is one of the most common injury conditions and requires a fast and accurate diagnosis process to assist optimal medical treatment. Examination using X-Ray images is the main method in identifying bone fractures, but the process of interpreting radiographic images has challenges, especially in Multi-class classification with similar fracture characteristics. This study aims to implement a transfer learning method based on the VGG16 architecture for Multi-class classification of bone fractures using X-Ray images. The dataset used consists of 11 classes, namely Avulsion Fracture, Comminuted Fracture, Fracture Dislocation, Greenstick Fracture, Hairline Fracture, Impacted Fracture, Longitudinal Fracture, Oblique Fracture, Pathological Fracture, Spiral Fracture, and Normal. The preprocessing stage includes resizing the image to 256 × 256 pixels, RGB conversion, VGG16 preprocessing, and data augmentation to increase the variety of the dataset. The model was built using pretrained VGG16 as a feature extractor with the addition of GlobalAveragePooling2D, Dense layer, BatchNormalization, and Dropout and fine-tuning was performed on several final layers. The evaluation results showed that the model obtained an accuracy of 98.40%, a macro precision of 97.95%, and a macro recall of 97.95%. In addition, most classes obtained accuracy values close to 100%. The results showed that the application of VGG16-based transfer learning was able to provide excellent classification performance on X-Ray images of bone fractures and was effective in improving the model's generalization ability in multi-class classification of medical images.
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[1] Badan Pusat Statistik (BPS), Statistik T Ransportasi Darat Land Transportation Statistics 2024, vol. 10. Badan Pusat Statistik, 2025. Accessed: Apr. 02, 2026. [Online]. Available: https://www.bps.go.id/id/publication/2025/12/01/ed7ff73d58fc0719ee3df145/statistik-transportasi-darat-2024.html
[2] J. Kesehatan Qamarul Huda et al., “Usia Dan Lokasi Fraktur Berhubungan Dengan Lama Rawat Pasca Bedah Open Reduction And Internal Fixation (ORIF) Ekstremitas Bawah,” vol. 13, no. 2, pp. 1–8, 2025, doi: 10.37824/jkqh.v13i1.2025.
[3] E. Suara, D. Retnaningsih, and E. P. Sugianto, “The Influence Of Pre-Operative Care On Pre-Operative Anxiety Of Fracture Patients,” p. 1724, 2025, doi: http://jurnal.globalhealthsciencegroup.com/index.php/JLH.
[4] A. Guermazi et al., “Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence,” Radiology, vol. 302, no. 3, pp. 627–636, Mar. 2022, doi: 10.1148/radiol.210937.
[5] D. Bolla and W. Bukaita, “Multi-Region Bone Fracture Detection in X-Ray Images using Deep Learning,” Med. Res. Arch., vol. 13, no. 12, 2025, doi: 10.18103/mra.v13i12.7099.
[6] N. Ramadanov et al., “Artificial intelligence-guided distal radius fracture detection on plain radiographs in comparison with human raters,” J. Orthop. Surg. Res., vol. 20, no. 1, Dec. 2025, doi: 10.1186/s13018-025-05888-9.
[7] R. Adhyaksa and B. Purnama, “Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images,” Jurnal RESTI, vol. 9, no. 1, pp. 118–129, 2025, doi: 10.29207/RESTI.V9I1.6101.
[8] S. Gupta, D. Sharma, D. Ahilya, and V. Vidyalaya, “Bone Fracture Classification Using Transfer Learning”.
[9] I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 3, May 2021, doi: 10.1007/S42979-021-00592-X.
[10] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets 2021 31:3, vol. 31, no. 3, pp. 685–695, Apr. 2021, doi: 10.1007/S12525-021-00475-2.
[11] Prof. Dr. Sugiyono, Metode Penelitian Kuantitatif. 2023.
[12] M. E. B. M. Mustafa A. Mafraji, “Computed Tomography (CT) - Special Subjects - MSD Manual Professional Edition.” Accessed: Apr. 05, 2026. [Online]. Available: https://www.msdmanuals.com/professional/special-subjects/principles-of-radiologic-imaging/computed-tomography-ct
[13] A. Elkohail et al., “Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration,” Cureus, Nov. 2025, doi: 10.7759/cureus.97674.
[14] M. A. Al-Fahrezi, “Pengaruh Augmentasi Data Terhadap Akurasi Pelatihan Model CNN untuk Klasifikasi Jenis Ikan,” JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, vol. 6, no. 2, pp. 177–185, Jun. 2025, doi: 10.62527/JITSI.6.2.471.
[15] C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data 2019 6:1, vol. 6, no. 1, pp. 60-, Jul. 2019, doi: 10.1186/S40537-019-0197-0.
[16] Karan, R. Firdaus, and H. Mukhtar, “Transfer Learning dengan CLAHE dan Sharpening filter untuk Deteksi Pneumonia pada Citra X-Ray,” Jurnal Fasilkom, vol. 16, no. 1, pp. 218–229, May 2026, doi: 10.37859/JF.V16I1.11374.
[17] L. Alzubaidi et al., “MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks”.
[18] T. Fatima and H. Soliman, “Application of VGG16 Transfer Learning for Breast Cancer Detection,” Information 2025, Vol. 16, Page 227, vol. 16, no. 3, p. 227, Mar. 2025, doi: 10.3390/INFO16030227.
[19] H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, “Transfer learning for medical image classification: a literature review,” BMC Med. Imaging, vol. 22, no. 1, p. 69, Dec. 2022, doi: 10.1186/S12880-022-00793-7.
[20] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data 2021 8:1, vol. 8, no. 1, pp. 53-, Mar. 2021, doi: 10.1186/S40537-021-00444-8.
[21] Purwono, A. Ma’arif, W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky, and Q. M. U. Haq, “Understanding of Convolutional Neural Network (CNN): A Review,” International Journal of Robotics and Control Systems, vol. 2, no. 4, pp. 739–748, 2022, doi: 10.31763/ijrcs.v2i4.888.
[22] I. Salehin and D. K. Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics 2023, Vol. 12, Page 3106, vol. 12, no. 14, p. 3106, Jul. 2023, doi: 10.3390/ELECTRONICS12143106.
[23] I. D. Mienye, T. G. Swart, G. Obaido, M. Jordan, and P. Ilono, “Deep Convolutional Neural Networks in Medical Image Analysis: A Review,” Information 2025, Vol. 16, Page 195, vol. 16, no. 3, p. 195, Mar. 2025, doi: 10.3390/INFO16030195.
[24] Y. Li, C. Liu, G. Niu, X. Luo, H. Ma, and Y. Zhao, “Error Detection and Correction of Mismatch Errors in M-Channel TIADCs Based on Genetic Algorithm Optimization,” Electronics 2022, Vol. 11, Page 2366, vol. 11, no. 15, p. 2366, Jul. 2022, doi: 10.3390/ELECTRONICS11152366.
[25] J. Lee, W. Wiratama, W. Lee, I. Marzuki, and D. Sim, “Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries,” Applied Sciences 2023, Vol. 13, Page 2485, vol. 13, no. 4, p. 2485, Feb. 2023, doi: 10.3390/APP13042485.
[26] H. Kwon, Z. Sandhu, Z. Sarwar, and O. M. Andacoglu, “Impact of Medicaid expansion on kidney transplantation in the State Oklahoma,” World J. Transplant., vol. 14, no. 3, p. 92981, Sep. 2024, doi: 10.5500/WJT.V14.I3.92981.
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