scholarly journals A novel machine learning‐based analytical framework for automatic detection of COVID ‐19 using chest X‐ray images

2021 ◽  
Author(s):  
Shikhar Johri ◽  
Mehendi Goyal ◽  
Sahil Jain ◽  
Manoj Baranwal ◽  
Vinay Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Analytical Framework ◽  
X Ray ◽  
Chest X Ray

scholarly journals An Integrated Framework with Machine Learning and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray

2021 ◽  
pp. 115152
Author(s):  
Mahbubunnabi Tamal ◽  
Maha Alshammari ◽  
Meernah Alabdullah ◽  
Rana Hourani ◽  
Hossain Abu Alola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
X Ray ◽  
Integrated Framework ◽  
Chest X Ray

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Distributed Data ◽  
X Ray ◽  
Uniform Distributions ◽  
Privacy Laws ◽  
Data Owner ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Automatic detection method of lung cancers including ground-glass opacities from chest x-ray CT images

2002 ◽  
Author(s):  
Toshiharu Ezoe ◽  
Hotaka Takizawa ◽  
Shinji Yamamoto ◽  
Akinobu Shimizu ◽  
Tohru Matsumoto ◽  
...  
Keyword(s):  
Detection Method ◽  
Automatic Detection ◽  
Ct Images ◽  
Ground Glass ◽  
Lung Cancers ◽  
X Ray ◽  
Chest X Ray

Chest X-Ray Image-Based Testing Using Machine Learning Techniques

Covid-19 ◽  
2021 ◽  
pp. 241-278
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Alaknanda Ashok ◽  
Amit Dumka ◽  
Anuj Bhardwaj
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

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