scholarly journals Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training

2021 ◽  
Vol 8 (06) ◽  
Author(s):  
Rafael B. Fricks ◽  
Francesco Ria ◽  
Hamid Chalian ◽  
Pegah Khoshpouri ◽  
Ehsan Abadi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs

scholarly journals Comparing different deep learning architectures for classification of chest radiographs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Keno K. Bressem ◽  
Lisa C. Adams ◽  
Christoph Erxleben ◽  
Bernd Hamm ◽  
Stefan M. Niehues ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Learning Architectures

scholarly journals 1140: CLASSIFICATION OF IDIOPATHIC PULMONARY FIBROSIS USING CHEST RADIOGRAPHS AND DEEP LEARNING APPROACH

2021 ◽  
Vol 50 (1) ◽  
pp. 568-568
Author(s):  
Quan Do ◽  
Kirill Lipatov ◽  
Michelle Herberts ◽  
Brian Pickering ◽  
Brian Bartholmai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Chest Radiographs ◽  
Learning Approach

scholarly journals Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs

2019 ◽  
Vol 32 (6) ◽  
pp. 925-930 ◽  
Author(s):  
Tae Kyung Kim ◽  
Paul H. Yi ◽  
Jinchi Wei ◽  
Ji Won Shin ◽  
Gregory Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Learning Method ◽  
Automated Classification

Prediction of Future Healthcare Expenses of Patients from Chest Radiographs Using Deep Learning: A Pilot Study

2021 ◽  
Author(s):  
Jae Ho Sohn ◽  
Yixin Chen ◽  
Dmytro Lituiev ◽  
Jaewon Yang ◽  
Karen Ordovas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Health Care Expenditure ◽  
Chest Radiographs ◽  
Cost Data ◽  
Imaging Data ◽  
Starting Point ◽  
Healthcare Expenses ◽  
Deep Learning Model

Abstract Our objective was to develop deep learning models with chest radiograph data to predict healthcare costs and classify top-50% spenders. 21,872 frontal chest radiographs were retrospectively collected from 19,524 patients with at least 1-year spending data. Among the patients, 11,003 patients had 3 years of cost data, and 1678 patients had 5 years of cost data. Model performances were measured with area under the receiver operating characteristic curve (ROC-AUC) for classification of top-50% spenders and Spearman ρ for prediction of healthcare cost. The best model predicting 1-year (N=21,872) expenditure achieved ROC-AUC of 0.806 [95% CI, 0.793-0.819] for top-50% spender classification and ρ of 0.561 [0.536-0.586] for regression. Similarly, for predicting 3-year (N=12,395) expenditure, ROC-AUC of 0.771 [0.750-0.794] and ρ of 0.524 [0.489-0.559]; for predicting 5-year (N=1,779) expenditure ROC-AUC of 0.729 [0.667-0.729] and ρ of 0.424 [0.324-0.529]. Our deep learning model demonstrated the feasibility of predicting health care expenditure as well as classifying top 50% healthcare spenders at 1, 3, and 5 year(s), implying the feasibility of combining deep learning with information-rich imaging data to uncover hidden associations that may allude physicians. Such a model can be a starting point of making an accurate budget in reimbursement models in healthcare industries.

Deep Learning-Based Tool for Automatic Feature Marking, Cropping, Visualization, and Classification of Chest Radiographs

2021 ◽  
Author(s):  
Geeta Rani ◽  
Akruti Sinha ◽  
Mahin Anup ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Deep Learning ◽  
Chest Radiographs

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market
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