An adaptive data processing framework for cost- effective covid-19 and pneumonia detection

Kin, Wai Lee and Ka, Renee Yin Chin (2021) An adaptive data processing framework for cost- effective covid-19 and pneumonia detection.

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Abstract

Medical imaging modalities have been showing great potentials for faster and efficient disease transmission control and containment. In the paper, we propose a costeffective COVID-19 and pneumonia detection framework using CT scans acquired from several hospitals. To this end, we incorporate a novel data processing framework that utilizes 3D and 2D CT scans to diversify the trainable inputs in a resource-limited setting. Moreover, we empirically demonstrate the significance of several data processing schemes for our COVID-19 and pneumonia detection network. Experiment results show that our proposed pneumonia detection network is comparable to other pneumonia detection tasks integrated with imaging modalities, with 93% mean AUC and 85.22% mean accuracy scores on generalized datasets. Additionally, our proposed data processing framework can be easily adapted to other applications of CT modality, especially for cost-effective and resource-limited scenarios, such as breast cancer detection, pulmonary nodules diagnosis, etc.

Item Type:	Proceedings
Keyword:	Covid-19 screening , Pneumonia detection , Data processing , Chest computerized tomography
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA1-43 General R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC581-951 Specialties of internal medicine
Department:	FACULTY > Faculty of Engineering
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	22 Apr 2022 16:16
Last Modified:	22 Apr 2022 16:16
URI:	https://eprints.ums.edu.my/id/eprint/32426

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