Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

2026-07-30

AI Summary

This study addresses the strain on healthcare resources during the COVID-19 pandemic by proposing an automated method for classifying COVID-19 from chest CT images. The approach introduces a novel end-to-end hybrid architecture that, for the first time, integrates 3D convolutional neural networks (CNNs) with 3D MLP-Mixer modules: the 3D CNN captures local spatial features, while the 3D MLP-Mixer models global long-range dependencies, effectively preserving both fine-grained details and structural context. Evaluated on a dataset of 1,205 CT scans, the model achieves a classification accuracy of 79.5%, significantly outperforming pure 3D CNN or simple MLP baselines. These results demonstrate the effectiveness and innovation of the proposed hybrid architecture in diagnosing viral pneumonia.

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.

Problem

• COVID-19
• chest CT volumes
• automated classification
• computer-aided diagnosis
• viral pneumonia

Innovation

• 3D MLP-Mixer
• hybrid model
• 3D CNN
• COVID-19 classification
• chest CT volumes