Research on cooperative classification of multimedia visual images based on deep machine learning model

2019 ◽  
Author(s):  
Shu-yi Yuchi ◽  
Shu Xu
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Visual Images ◽  
Machine Learning Model

A Machine Learning Model for Automated Classification of Sleep Stages Using Polysomnography Signals

2021 ◽  
pp. 209-222
Author(s):  
Santosh Kumar Satapathy ◽  
Hari Kishan Kondaveeti ◽  
D. Loganathan ◽  
S. Sharathkumar
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Sleep Stages ◽  
Automated Classification ◽  
Machine Learning Model

Classification of Lathe’s Cutting Tool Wear Based on an Autonomous Machine Learning Model

2021 ◽  
Author(s):  
Thiago E. Fernandes ◽  
Matheus A. M. Ferreira ◽  
Guilherme P. C. de Miranda ◽  
Alexandre F. Dutra ◽  
Matheus P. Antunes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Cutting Tool ◽  
Learning Model ◽  
Cutting Tool Wear ◽  
Machine Learning Model ◽  
Autonomous Machine

scholarly journals COVID-19 Classification of X-ray Images Using Deep Neural Networks

2020 ◽  
Author(s):  
Elisha Goldstein ◽  
Daphna Keidar ◽  
Daniel Yaron ◽  
Yair Shachar ◽  
Ayelet Blass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Receiver Operating Characteristic ◽  
Deep Neural Networks ◽  
Operating Characteristic ◽  
Learning Model ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray

AbstractBackgroundIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts.PurposeThe purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.Materials and MethodsIn this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.ResultsThe dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.ConclusionDeep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. Moreover, the image embeddings learned by the network can be used to retrieve images with similar lung findings.SummaryDeep Neural Networks and can be used to reliably predict chest X-ray images as positive for coronavirus disease 2019 (COVID-19) or as negative for COVID-19.Key ResultsA machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for coronavirus disease 2019 with accuracy of 89.7%, sensitivity of 87.1% and area under receiver operating characteristic curve of 0.95.A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.

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