Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images

Resky Adhyaksa; Bedy Purnama

doi:10.29207/resti.v9i1.6101

Resky Adhyaksa Telkom University
Bedy Purnama Telkom University

DOI: https://doi.org/10.29207/resti.v9i1.6101

Keywords: Deep Learning, Bone Fracture, X-Ray Images, Convolutional Neural Network, VGG16

Abstract

The purpose of this research is to determine whether or not a deep learning model called VGG16 can automatically identify bone fractures in X-ray pictures. The dataset, sourced from Kaggle, includes 10,522 images of human hand and foot bones, which underwent preprocessing steps such as normalization and resizing to 224x224 pixels to enhance data quality. The study utilizes the VGG16 architecture, pre-trained on ImageNet, as a base model, with transfer learning applied to adapt the model for fracture detection by fine-tuning its weights. This architecture consists of five blocks of convolutional and max-pooling layers to effectively extract and enhance information from the images for precise classification. The training and testing phases utilized an 80:20 split of the data, employing binary cross-entropy as the loss function and the Adam optimizer for efficient weight updates. The model achieved high performance, with an accuracy of 99.25%, precision of 98.62%, recall of 98.88%, and an F1-score of 99.16% over 25 epochs with a batch size of 128. Experimental results indicate that smaller batch sizes generally enhance accuracy and reduce loss values, with batch sizes of 128 and 16 yielding optimal performance. The study's findings underscore the potential of VGG16 in improving diagnostic accuracy and reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency.

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