id:"swepub:oai:DiVA.org:su-219237"
Sökning: id:"swepub:oai:DiVA.org:su-219237" > COVID-19 detection ...
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03591naa a2200409 4500 | |
| 001 | oai:DiVA.org:su-219237 | |
| 003 | SwePub | |
| 008 | 230718s2023 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2192372 URI |
| 024 | 7 | a https://doi.org/10.1109/CBMS58004.2023.002942 DOI |
| 040 | a (SwePub)su | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a kon2 swepub-publicationtype |
| 100 | 1 | a Alam, Mahbub Ulu Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)maul8511 |
| 245 | 1 0 | a COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning |
| 264 | 1 | c 2023 |
| 338 | a print2 rdacarrier | |
| 520 | a COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
| 653 | a COVID-19 Detection | |
| 653 | a Thermal Image | |
| 653 | a Tabular Medical Data | |
| 653 | a Multi-Modality | |
| 653 | a Machine Learning | |
| 653 | a Deep Learning | |
| 653 | a Internet of Medical Things | |
| 653 | a data- och systemvetenskap | |
| 653 | a Computer and Systems Sciences | |
| 700 | 1 | a Hollmén, Jaakkou Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)jaho8578 |
| 700 | 1 | a Rahmani Chianeh, Rahimu Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)rrahm |
| 710 | 2 | a Stockholms universitetb Institutionen för data- och systemvetenskap4 org |
| 773 | 0 | t 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)g , s. 646-653q <646-653 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-219237 |
| 856 | 4 8 | u https://doi.org/10.1109/CBMS58004.2023.00294 |
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