A Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis
14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1985480
ABSTRACT
Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies;such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB;ii) when tested, or when applied, through transfer learning, to other public databases. © 2022 IEEE.
3D chest CT scan analysis; COV19-CT-DB database; COVID-19 diagnosis; dynamic routing; feature alignment; mask layer; medical imaging; RACNet deep neural network; Computerized tomography; Database systems; Deep neural networks; Diagnosis; Internet protocols; Memory architecture; Multilayer neural networks; Network architecture; Network routing; 3d chest CT scan analyse; Chest CT scans; COVID-19 diagnose; CT-scan; Neural architectures; COVID-19
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2022
Year:
2022
Document Type:
Article
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