Pneumonia Diagnosis Using Chest X-ray Images and Machine Learning

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.

Download Full-text

Deep Learning for COVID-19: COVID-19 Detection Based on Chest X-Ray Images by the Fusion of Deep Learning and Machine Learning Techniques

Understanding COVID-19: The Role of Computational Intelligence - Studies in Computational Intelligence ◽

10.1007/978-3-030-74761-9_21 ◽

2021 ◽

pp. 471-500

Author(s):

Swati V. Shinde ◽

Deepak T. Mane

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Learning Techniques ◽

X Ray ◽

Learning Techniques ◽

Chest X Ray

Download Full-text

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

Covid-19 ◽

10.1201/9781003131410-6 ◽

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

Download Full-text

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

SLAS TECHNOLOGY Translating Life Sciences Innovation ◽

10.1177/2472630320958376 ◽

2020 ◽

Vol 25 (6) ◽

pp. 553-565 ◽

Cited By ~ 1

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.

Download Full-text

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

eLife ◽

10.7554/elife.70640 ◽

2021 ◽

Vol 10 ◽

Author(s):

Emirena Garrafa ◽

Marika Vezzoli ◽

Marco Ravanelli ◽

Davide Farina ◽

Andrea Borghesi ◽

...

Keyword(s):

Machine Learning ◽

Hospital Mortality ◽

Learning Model ◽

Hospital Death ◽

Web Based ◽

X Ray ◽

Machine Learning Model ◽

Chest X Ray ◽

Blood Analytes ◽

Second Wave

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Download Full-text

ANALYZING CHEST X-RAY TO DIFFERENTIATE SYMPTOMS OF COVID-19 THROUGH ML APPROACH

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i12.042 ◽

2021 ◽

Vol 5 (12) ◽

Author(s):

Deepali R Deshpande ◽

Raj L Shah ◽

Anish N Shaha

Keyword(s):

Machine Learning ◽

Learning Model ◽

Time Constraints ◽

X Rays ◽

Rt Pcr ◽

X Ray ◽

Social Distancing ◽

Machine Learning Model ◽

Chest X Ray

The motive behind the project is to build a machine learning model for detection of Covid-19. Using this model, it is possible to classify images of chest x-rays into normal patients, pneumatic patients, and covid-19 positive patients. This CNN based model will help drastically to save time constraints among the patients. Instead of relying on limited RT-PCR kits, just a simple chest x-ray can help us determine health of the patient. Not only we get immediate results, but we can also practice social distancing norms more effectively.

Download Full-text