Deep Learning and Cloud-Based Computation for Cervical Spine Fracture Detection System

Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 9; p. 2056
Main Authors Chlad, Pawel, Ogiela, Marek R
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 29.04.2023
Subjects
Artificial neural networks
Cloud computing
Computed tomography
CT imaging
Deep learning
Disability
Fractures
Machine learning
Medical imaging
Medical personnel
Model accuracy
Neural networks
Older people
Spine
Poland
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Summary:Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by hand is both time consuming and error prone. Deep learning (DL) techniques can offer more effective methods for detecting fractures, and with the increasing availability of ubiquitous cloud resources, implementing such systems worldwide is becoming more feasible. This study aims to evaluate the effectiveness of ViT for detecting cervical spine fractures. Data gathered during the research indicates that ViT models are suitable for large-scale automatic detection system implementation. The model achieved 98% accuracy and was easy to train while also being easily explainable.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12092056