Caps-3WD: A three-way diagnostic CapsNet for COVID-19
2nd International Conference on Medical Imaging and Additive Manufacturing, ICMIAM 2022
; 12179, 2022.
Article
in English
| Scopus | ID: covidwho-2029447
ABSTRACT
Pulmonary medical image processing is an effective diagnostic method for COVID-19, and CapsNet-based methods have achieved good performance. However, as cost-blind methods, these diagnostic methods only consider immediate and deterministic decisions, which easily lead to misdiagnosis and high costs. Therefore, based on a revised CapsNet, we propose a cost-sensitive three-way decision (3WD) method for COVID-19 diagnosis, named as Caps-3WD. To enhance the feature extraction ability for pneumonia areas, we introduce a Restage module to improve convolution layer of the original CapsNet. Further, to lighten the model, we introduce depth wise separable convolution to reconstruct decoder. Additionally, three options are considered in the decision set infected, normal, and suspected, which are given different costs, respectively. The lowest-cost decision is chosen for each input. In the experimental analysis, we compare Caps-3WD with CNN-based and CapsNet-based methods on COVID-CXR dataset, which proves the effectiveness of 3WD and the superiority of Caps-3WD in COVID-19 diagnosis. © 2022 SPIE. Downloading of the is permitted for personal use only.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Medical Imaging and Additive Manufacturing, ICMIAM 2022
Year:
2022
Document Type:
Article
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