Projects per year
Up to now, the COVID-19 has been sweeping across all over the world, which has affected individual’s lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This paper presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. Context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97%, and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99%, and specificity of 99.4% of the ResNet-50.
|Journal||IEEE Internet of Things Journal|
|Publication status||E-pub ahead of print - 2021|
|MoE publication type||A1 Journal article-refereed|
- Feature extraction
- Computed tomography
- Solid modeling
- Internet of Things
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INTERVENE: International consortium for integrative genomics prediction
01/01/2021 → 31/12/2025
Project: EU: Framework programmes funding
Data Literacy for Responsible Decision-Making
Marttinen, P., Gröhn, T., Honkamaa, J., Kumar, Y., Ji, S., Raj, V., Ojala, F., Pöllänen, A. & Tiwari, P.
01/10/2020 → 30/09/2023
Project: Academy of Finland: Strategic research funding
eMOM: CleverHealth Network: eMOM GDM -Project
Marttinen, P., Alizadeh Ashrafi, R., Hizli, C. & Zhang, G.
05/02/2018 → 31/01/2023
Project: Business Finland: Other research funding