https://github.com/anondo1969/fedsemicoviddetector
Repository for the journal article, 'Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application', Mahbub Ul Alam, Rahim Rahmani. Sensors 21, no. 15: 5025, https://doi.org/10.3390/s21155025.
https://github.com/anondo1969/fedsemicoviddetector
clinical-decision-support-system covid-19-detection deep-learning electronic-health-records federated-learning health-informatics internet-of-medical-things iomt lungs-segmentation-detection machine-learning multi-modality raspberry-pi smart-healthcare
Last synced: 5 months ago
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Repository for the journal article, 'Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application', Mahbub Ul Alam, Rahim Rahmani. Sensors 21, no. 15: 5025, https://doi.org/10.3390/s21155025.
- Host: GitHub
- URL: https://github.com/anondo1969/fedsemicoviddetector
- Owner: anondo1969
- License: mit
- Created: 2024-01-16T01:42:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-06T10:58:54.000Z (5 months ago)
- Last Synced: 2025-05-08T16:55:24.461Z (5 months ago)
- Topics: clinical-decision-support-system, covid-19-detection, deep-learning, electronic-health-records, federated-learning, health-informatics, internet-of-medical-things, iomt, lungs-segmentation-detection, machine-learning, multi-modality, raspberry-pi, smart-healthcare
- Language: Python
- Homepage: https://doi.org/10.3390/s21155025
- Size: 4.24 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FedSemiCovidDetector
**[Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application](https://doi.org/10.3390/s21155025)**, Mahbub Ul Alam, Rahim Rahmani. Sensors 21, no. 15: 5025, https://doi.org/10.3390/s21155025. (**[presentation poster](http://mahbub.blogs.dsv.su.se/files/2022/05/poster_Mahbub_Ul_Alam.pdf)**)
## Abstract
Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, 𝐹𝑠𝑐𝑜𝑟𝑒 for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
## [Poster presented at the first Inria-DFKI European Summer School on AI (IDAI 2021)](https://idessai.eu/posters-and-demos-2021/)
## Citation
Please acknowledge the following work in papers or derivative software:
Alam MU, Rahmani R. Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application. Sensors. 2021; 21(15):5025. https://doi.org/10.3390/s21155025
### Bibtex Format for Citation
```
@Article{s21155025,
AUTHOR = {Alam, Mahbub Ul and Rahmani, Rahim},
TITLE = {Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {15},
ARTICLE-NUMBER = {5025},
URL = {https://www.mdpi.com/1424-8220/21/15/5025},
PubMedID = {34372262},
ISSN = {1424-8220},
DOI = {10.3390/s21155025}
}
```