Artificial Intelligence based Multi-sensor COVID-19 Screening Framework
Gorde:
| Egileak: | , , |
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| Formatua: | artículo original |
| Egoera: | Versión publicada |
| Argitaratze data: | 2022 |
| Deskribapena: | Many countries are struggling for COVID-19 screening resources which arises the need for automatic and low-cost diagnosis systems which can help to diagnose and a large number of tests can be conducted rapidly. Instead of relying on one single method, artificial intelligence and multiple sensors based approaches can be used to decide the prediction of the health condition of the patient. Temperature, oxygen saturation level, chest X-ray and cough sound can be analyzed for the rapid screening. The multi-sensor approach is more reliable and a person can be analyzed in multiple feature dimensions. Deep learning models can be trained with multiple chest x-ray images belonging to different categories to different health conditions i.e. healthy, COVID-19 positive, pneumonia, tuberculosis, etc. The deep learning model will extract the features from the input images and based on that test images will be classified into different categories. Similarly, cough sound and short talk can be trained on a convolutional neural network and after proper training, input voice samples can be differentiated into different categories. Artificial based approaches can help to develop a system to work efficiently at a low cost. |
| Herria: | Portal de Revistas TEC |
| Erakundea: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Hizkuntza: | Inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/6460 |
| Sarrera elektronikoa: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6460 |
| Gako-hitza: | Convolutional Neural Network COVID-19 detection Deep Learning Multi-sensor. Red neuronal convolucional detección COVID-19 aprendizaje profundo sensor múltiple |