Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images

Deep learning-based automated spine fracture type identification with Clinically validated GAN generated CT images

AbstractAutomatic type identification of sub-axial spine fractures is of prime importance for orthopaedicians to reduce image interpretation time and increase patient care time. But identifying fracture types is challenging due to imbalanced datasets. In this work, CT scan images of fractured spine...

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Bibliographic Details
Main Authors: Sindhura D. N., Radhika M. Pai, Shyamasunder N. Bhat, Manohara Pai M. M.
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Cogent Engineering
Subjects:
Classification
clinical evaluation
computed tomography (CT) images
Deep Convolutional Generative Adversarial Network (DCGAN)
deep learning
spine fracture
Online Access:https://www.tandfonline.com/doi/10.1080/23311916.2023.2295645

Internet

https://www.tandfonline.com/doi/10.1080/23311916.2023.2295645

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