Volume 22, Issue 4 (JIAEEE Vol.22 No.4 2025)
Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(4): 60-68 |
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Andi M, Mahmoudi P. Lightweight Bone Fracture Detection in Radiographic Images Using an Optimized MobileNetV3 Approach. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (4) :60-68
URL: http://jiaeee.com/article-1-1852-en.html
URL: http://jiaeee.com/article-1-1852-en.html
Department of Computer Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
Abstract: (592 Views)
Bone fractures are among the most common skeletal injuries, particularly in high-pressure sports environments and crowded emergency departments, where rapid and accurate diagnosis is of great importance. Delays or errors in fracture detection may lead to impaired healing, mobility limitations, and increased treatment costs. In this study, we propose a lightweight system based on an optimized version of MobileNetV3 to classify radiographic images into two categories: fractured and non-fractured. A dataset of 10,580 radiographs from different anatomical regions was used for training. The images were resized to 224×224 pixels, normalized, and fed into the network. The architecture was redesigned by incorporating global average pooling, fully connected layers, and regularization techniques to mitigate overfitting. During training, the AdamW optimizer with a learning rate of 0.0001 was employed, where the initial layers were frozen and the final layers were fine-tuned to improve performance. Evaluation on 506 test images demonstrated an accuracy of 99.21%, which is comparable to or even superior to some heavier architectures. Owing to its lightweight nature, the proposed system can be deployed on low-resource devices such as smartphones and medical tablets, making it a fast and reliable assistant for radiologists in emergency rooms, clinics, and crowded clinical settings.
Keywords: Fracture detection, Radiographic images, Deep learning, MobileNetV3, Binary classification
Type of Article: Research |
Subject:
Electronic
Received: 2025/08/21 | Accepted: 2025/11/3 | Published: 2026/01/22
Received: 2025/08/21 | Accepted: 2025/11/3 | Published: 2026/01/22
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