Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
; 12465, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20240716
ABSTRACT
This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.
3D CNN; 3D MLP-Mixer; computer-aided diagnosis; COVID-19; hybrid classification model; 3D modeling; Classification (of information); Computer aided diagnosis; Computerized tomography; Convolutional neural networks; Image classification; Medical imaging; Mixers (machinery); 3d convolutional neural network; Automated classification; Chest CT; Classification methods; Classification models; Convolutional neural network; CT volume; Hybrid classification
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio experimental
Idioma:
Inglés
Revista:
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Año:
2023
Tipo del documento:
Artículo
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