Bone Fracture Detection System using CNN Algorithm

2019 ◽  
Author(s):  
Siva S Sinthura ◽  
Y. Prathyusha ◽  
K. Harini ◽  
Y. Pranusha ◽  
B. Poojitha
Keyword(s):  
Bone Fracture ◽  
Detection System ◽  
Fracture Detection

scholarly journals Computer Based Bone Breakage Detection using Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 3695-3698
Keyword(s):  
Machine Learning ◽  
Bone Fracture ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Fracture Detection ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Based

X-Ray is one of the most commonly used medium to extract the images of any bone in the body.Fracture of a bone is most common in recent days due to accidents or any means.In order to detect whether there is a fracture or not the orthopaedics suggest for x-ray.In many places due to more patients there might be a delay of doctor consult which may leads to the increase in the severity of problem.In order to avoid this we have proposed an automatic bone fracture detection system where a system is trained about the fractures and further used to detect the fractures in a bone in the x-ray images.ANN,PNN.BPNN are the classifiers used for bone fracture detection where BPNN has given more prominent results compared to ANN and PNN with an accuracy of 82%.

scholarly journals Bone Fracture Detection System using Image Processing and Matlab

2019 ◽  
Vol 8 (12) ◽  
pp. 1459-1461
Keyword(s):  
Image Processing ◽  
Bone Fracture ◽  
Detection System ◽  
Main Stage ◽  
Fracture Detection ◽  
Efficient Recognition ◽  
The Way

Quickly creating innovations are developing each day in various fields, particularly in restorative condition. Notwithstanding, still some old strategies are very famous. XRays are one of these systems for identification of bone cracks. By the way, here and there the span of breaks isn't huge and couldn't be recognized effectively. So for the efficient recognition of the crack has become more important . This venture plans to build up an sharp characterization framework that would be equipped for identifying and characterizing the bone cracks. The created framework involves two important stages. In the main stage, the pictures of the breaks are prepared utilizing distinctive picture handling systems in request to identify their area and shapes and the following stage is the arrangement stage, where the sample image is filtered through various filtration stages to obtain the crack effectively, the framework was tried on various bone break pictures and the outcomes show high proficiency what's more, an arrangement rate.

scholarly journals Rib fracture detection system based on deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liding Yao ◽  
Xiaojun Guan ◽  
Xiaowei Song ◽  
Yanbin Tan ◽  
Chun Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Rib Fracture ◽  
Diagnostic Efficiency ◽  
Fracture Detection ◽  
Deep Learning Algorithm ◽  
Experienced Radiologist ◽  
Comparable Performance ◽  
Detection And Diagnosis

AbstractRib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model’s clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists’ workload in the clinical practice.

scholarly journals Identification of Bone Fragmentation in X-Ray Images using Contour Detection Algorithm

2019 ◽  
Vol 8 (9) ◽  
pp. 2121-2124
Keyword(s):  
Edge Detection ◽  
Bone Fracture ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Crack Identification ◽  
Fracture Detection ◽  
Canny Edge Detection ◽  
X Ray ◽  
Base Paper ◽  
Canny Edge

The crack can occur in any bone ofour body. Broken bone is a bone condition that endured a breakdown of bone trustworthiness. The Fracture can't recognize effortlessly by the bare eye, so it is found in the x-beam images. The motivation behind this task is to find the precise territory where the bone fracture happens utilizing X-Ray Bone Fracture Detection by Canny Edge Detection Method. Shrewd Edge Detection technique is an ideal edge identification calculation on deciding the finish of a line with alterable limit and less error rate. The reproduction results have indicated how canny edge detection can help decide area of breaks in x-beam images. In the base paper, the cracked bit is chosen physically to defeat this downside, the proposed technique identify the bone fracture consequently and furthermore it quantifies the parameter like length of the crack, profundity of the fracture and the situation of the crack as for even and vertical pivot. The outcome demonstrates that the proposed technique for crack identification is better. The outcomes demonstrate that calculation is 91% exact and effective

Clinical evaluation of AI software for rib fracture detection and its impact on junior radiologist performance

2021 ◽  
pp. 028418512110438
Author(s):  
Xiang Liu ◽  
Dijia Wu ◽  
Huihui Xie ◽  
Yufeng Xu ◽  
Lin Liu ◽  
...  
Keyword(s):  
Detection System ◽  
Ct Images ◽  
Diagnostic Efficiency ◽  
Fracture Detection ◽  
Patient Level ◽  
Primary Endpoints ◽  
Significant Difference ◽  
Secondary Endpoints ◽  
Lesion Level ◽  
Missed Diagnosis

Background The detection of rib fractures (RFs) on computed tomography (CT) images is time-consuming and susceptible to missed diagnosis. An automated artificial intelligence (AI) detection system may be helpful to improve the diagnostic efficiency for junior radiologists. Purpose To compare the diagnostic performance of junior radiologists with and without AI software for RF detection on chest CT images. Materials and methods Six junior radiologists from three institutions interpreted 393 CT images of patients with acute chest trauma, with and without AI software. The CT images were randomly split into two sets at each institution, with each set assigned to a different radiologist First, the detection of all fractures (AFs), including displaced fractures (DFs), non-displaced fractures and buckle fractures, was analyzed. Next, the DFs were selected for analysis. The sensitivity and specificity of the radiologist-only and radiologist-AI groups at the patient level were set as primary endpoints, and secondary endpoints were at the rib and lesion level. Results Regarding AFs, the sensitivity difference between the radiologist-AI group and the radiologist-only group were significant at different levels (patient-level: 26.20%; rib-level: 22.18%; lesion-level: 23.74%; P < 0.001). Regarding DFs, the sensitivity difference was 16.67%, 14.19%, and 16.16% at the patient, rib, and lesion levels, respectively ( P < 0.001). No significant difference was found in the specificity between the two groups for AFs and DFs at the patient and rib levels ( P > 0.05). Conclusion AI software improved the sensitivity of RF detection on CT images for junior radiologists and reduced the reading time by approximately 1 min per patient without decreasing the specificity.

Multiple Reception Field Network with Attention Module on Bone Fracture Detection Task

2021 ◽  
Author(s):  
Mengxuan Wang ◽  
Guoshan Zhang ◽  
Bin Guan ◽  
Mingyang Xia ◽  
Xinbo Wang
Keyword(s):  
Bone Fracture ◽  
Detection Task ◽  
Fracture Detection ◽  
Field Network

Combining classifiers for bone fracture detection in X-ray images

2005 ◽  
Author(s):  
Vineta Lai Fun Lum ◽  
Wee Kheng Leow ◽  
Ying Chen ◽  
Tet Sen Howe ◽  
Meng Ai Png
Keyword(s):  
Bone Fracture ◽  
Fracture Detection ◽  
Combining Classifiers ◽  
X Ray
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