Automated universal fractures detection in X-ray images based on deep learning approach

Lu, Shuzhen; Wang, Shengsheng; Wang, Guangyao

doi:10.1007/s11042-022-13287-z

Cited by 9 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By incorporating HyperColumn-CBAM structures into the EfficientNet-B0 and DenseNet169 models, they achieved an accuracy of 87.50%. Lu et al [48] developed a universal fracture detection system through deep CNN methods. Initially, image enhancement techniques were applied to enhance image quality.…”

Section: Study Methodsmentioning

confidence: 99%

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

Kaya,

Taşcı

2023

Diagnostics

View full text Add to dashboard Cite

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.

show abstract

Section: Study Methodsmentioning

confidence: 99%

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

Kaya,

Taşcı

2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…In addition to chest X-ray images, studies to detect femoral neck fractures using other X-ray images and to classify displaced and non-displaced fractures have also been reported [ 7 , 8 ]. Another study proposed an automatic fracture detection system based on the Ada-ResNet backbone network in various X-ray images [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs

Park,

Lee,

Kim

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.

show abstract

“…In addition, in [ 21 ], a universal fracture detection CAD system was developed on X-ray images based on the deep learning method. Firstly, we design an image preprocessing method to improve the poor quality of these X-ray images and employ several data augmentation strategies to enlarge the used dataset.…”

Section: Introductionmentioning

confidence: 99%

Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map

et al. 2023

View full text Add to dashboard Cite

Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.

show abstract

Automated universal fractures detection in X-ray images based on deep learning approach

Cited by 9 publications

References 37 publications

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs

Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map

Contact Info

Product

Resources

About