Ulnar variance detection from radiographic images using deep learning
Abstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. It is a critical factor in helping to diagnose wrist disorders. The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance...
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SpringerOpen
2025-02-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01072-2 |
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| author | Sahar Nooh Abdelrahim Koura Mohammed Kayed |
| author_facet | Sahar Nooh Abdelrahim Koura Mohammed Kayed |
| author_sort | Sahar Nooh |
| collection | DOAJ |
| description | Abstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. It is a critical factor in helping to diagnose wrist disorders. The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance, negative ulnar variance, and neutral ulnar variance. Conventional or manual methods of measuring ulnar variance are long and time-consuming. With the urgent need for high efficiency and high speed, achieving more accurate diagnoses has become essential. In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. Specifically, U-Net is used in the segmentation of ulna and radius bones, while DenseNets are applied to classify the type of ulnar variance. The essential contribution of this work is collecting a dataset of fully annotated wrist radiographs that are specific to this topic, which can be used as a resource to train and validate our models. Another contribution of this paper is optimizing the DenseNets model's hyperparameters to enhance its performance. Our model achieved a segmentation accuracy of 97.7% and an ulna variance classification accuracy of 92.1%. It outperformed previous deep learning-based methods in automatically segmenting the ulna and radius. This advancement not only reduces diagnosis time but also improves result reliability. |
| format | Article |
| id | doaj-art-3821cf2a57da4ab58fc47f2764ec76eb |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-3821cf2a57da4ab58fc47f2764ec76eb2025-02-09T12:41:19ZengSpringerOpenJournal of Big Data2196-11152025-02-0112111410.1186/s40537-025-01072-2Ulnar variance detection from radiographic images using deep learningSahar Nooh0Abdelrahim Koura1Mohammed Kayed2Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef UniversityComputer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef UniversityComputer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef UniversityAbstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. It is a critical factor in helping to diagnose wrist disorders. The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance, negative ulnar variance, and neutral ulnar variance. Conventional or manual methods of measuring ulnar variance are long and time-consuming. With the urgent need for high efficiency and high speed, achieving more accurate diagnoses has become essential. In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. Specifically, U-Net is used in the segmentation of ulna and radius bones, while DenseNets are applied to classify the type of ulnar variance. The essential contribution of this work is collecting a dataset of fully annotated wrist radiographs that are specific to this topic, which can be used as a resource to train and validate our models. Another contribution of this paper is optimizing the DenseNets model's hyperparameters to enhance its performance. Our model achieved a segmentation accuracy of 97.7% and an ulna variance classification accuracy of 92.1%. It outperformed previous deep learning-based methods in automatically segmenting the ulna and radius. This advancement not only reduces diagnosis time but also improves result reliability.https://doi.org/10.1186/s40537-025-01072-2Ulnar varianceDeep learningCNNDenseNetsU-NetSegmentation |
| spellingShingle | Sahar Nooh Abdelrahim Koura Mohammed Kayed Ulnar variance detection from radiographic images using deep learning Journal of Big Data Ulnar variance Deep learning CNN DenseNets U-Net Segmentation |
| title | Ulnar variance detection from radiographic images using deep learning |
| title_full | Ulnar variance detection from radiographic images using deep learning |
| title_fullStr | Ulnar variance detection from radiographic images using deep learning |
| title_full_unstemmed | Ulnar variance detection from radiographic images using deep learning |
| title_short | Ulnar variance detection from radiographic images using deep learning |
| title_sort | ulnar variance detection from radiographic images using deep learning |
| topic | Ulnar variance Deep learning CNN DenseNets U-Net Segmentation |
| url | https://doi.org/10.1186/s40537-025-01072-2 |
| work_keys_str_mv | AT saharnooh ulnarvariancedetectionfromradiographicimagesusingdeeplearning AT abdelrahimkoura ulnarvariancedetectionfromradiographicimagesusingdeeplearning AT mohammedkayed ulnarvariancedetectionfromradiographicimagesusingdeeplearning |