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p-Index From 2021 - 2026
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This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Ei Phyu Sin Win
Mandalay Technological University, Myanmar
Computer Science & IT Electrical & Electronics Engineering Engineering

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Performance Analysis of Vision Transformer (ViT), ResNet50, and MobileNetV3 Large in Multiclass Bone Fracture Classification Ei Phyu Sin Win; Phyo Thu Zar Tun
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i3.5150

Abstract

Automated classification of bone fractures has become a cornerstone of modern emergency radiology, significantly enhancing diagnostic speed and precision. This study evaluates the comparative efficacy of three leading deep learning frameworks ResNet50, MobileNetV3, and Vision Transformer (ViT) using a diverse dataset that includes various fracture modalities, healthy X-rays, and non-radiological images.The experimental data reveals that the Vision Transformer (ViT) attained the highest diagnostic accuracy at 95%, marginally outperforming MobileNetV3 and ResNet50, which both achieved 94%. While all three models demonstrated flawless reliability (100%) in identifying Forteen Classes Bone categories, their performance diverged when analyzing complex fracture patterns.
Co-Authors Phyo Thu Zar Tun