Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs

2022 ◽  
Vol 23 ◽  
Author(s):  
Jae Won Choi ◽  
Yeon Jin Cho ◽  
Ji Young Ha ◽  
Yun Young Lee ◽  
Seok Young Koh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skull Fractures ◽  
Plain Radiographs ◽  
Assisted Diagnosis

scholarly journals Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoshuo Li ◽  
Wenjun Tan ◽  
Pan Liu ◽  
Qinghua Zhou ◽  
Jinzhu Yang
Keyword(s):  
Deep Learning ◽  
Medical Personnel ◽  
Classification Performance ◽  
Cascade Classifier ◽  
Data Set ◽  
Global Pandemic ◽  
Good Classification Performance ◽  
Ct Image Analysis ◽  
Assisted Diagnosis ◽  
Novel Coronavirus

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

scholarly journals Deep learning predicts knee osteoarthritis progression from plain radiographs

2019 ◽  
Vol 27 ◽  
pp. S397-S398 ◽  
Author(s):  
A. Tiulpin ◽  
S. Klein ◽  
S. Bierma-Zeinstra ◽  
J. Thevenot ◽  
J. van Meurs ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Osteoarthritis ◽  
Plain Radiographs ◽  
Osteoarthritis Progression

scholarly journals Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002699 ◽  
Author(s):  
Nicholas Bien ◽  
Pranav Rajpurkar ◽  
Robyn L. Ball ◽  
Jeremy Irvin ◽  
Allison Park ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Assisted Diagnosis ◽  
Knee Magnetic Resonance Imaging

scholarly journals Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

2022 ◽  
Vol 12 (1) ◽  
pp. 738
Author(s):  
Akella S. Narasimha Raju ◽  
Kayalvizhi Jayavel ◽  
Tulasi Rajalakshmi
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Premalignant Lesions ◽  
Computer Assisted ◽  
Screening Methods ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

scholarly journals A new deep learning model for assisted diagnosis on electrocardiogram

2019 ◽  
Vol 16 (4) ◽  
pp. 2481-2491 ◽  
Author(s):  
Eric Ke Wang ◽  
◽  
liu Xi ◽  
Ruipei Sun ◽  
Fan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Assisted Diagnosis ◽  
Deep Learning Model

scholarly journals Stroke Lesion Detection and Analysis in MRI Images Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shujun Zhang ◽  
Shuhao Xu ◽  
Liwei Tan ◽  
Hongyan Wang ◽  
Jianli Meng
Keyword(s):  
Ischemic Stroke ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Brain Mri ◽  
Lesion Detection ◽  
Learning Ability ◽  
Massive Data ◽  
Stroke Patients ◽  
Stroke Lesion ◽  
Assisted Diagnosis

Stroke is a kind of cerebrovascular disease that heavily damages people’s life and health. The quantitative analysis of brain MRI images plays an important role in the diagnosis and treatment of stroke. Deep neural networks with massive data learning ability supply a powerful tool for lesion detection. In order to study the property of the stroke lesions and complete intelligent automatic detection, we collaborated with two authoritative hospitals and collected 5,668 brain MRI images of 300 ischemic stroke patients. All the lesion regions in the images were accurately labeled by professional doctors to ensure the authority and effectiveness of the data. Three categories of deep learning object detection networks including Faster R-CNN, YOLOV3, and SSD are applied to implement automatic lesion detection with the best precision of 89.77%. Meanwhile, statistical analysis of the locations, shapes of the lesions, and possible related diseases is conducted with valid conclusions. The research contributes to the intelligent assisted diagnosis and prevention and treatment of ischemic stroke.

Maintaining the spatial relation to improve deep-learning-assisted diagnosis for magnetic resonace imaging of the knee

2020 ◽  
Author(s):  
W Nikolas ◽  
L Jan ◽  
M Carina ◽  
Rüdiger von Eisenhart-Rothe ◽  
B Rainer
Keyword(s):  
Deep Learning ◽  
Spatial Relation ◽  
Assisted Diagnosis
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