CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classification

TL;DR

This paper introduces CECT, a novel ensemble CNN-transformer model for COVID-19 image classification that captures multi-scale features and demonstrates high accuracy and generalization on public datasets.

Contribution

The paper presents a controllable ensemble CNN-transformer architecture that captures multi-scale features without complex modules and controls local feature contributions.

Findings

Achieves 98.1% accuracy on intra-dataset evaluation

Attains 90.9% accuracy on unseen datasets

Outperforms existing state-of-the-art methods

Abstract

The COVID-19 pandemic has resulted in hundreds of million cases and numerous deaths worldwide. Here, we develop a novel classification network CECT by controllable ensemble convolutional neural network and transformer to provide a timely and accurate COVID-19 diagnosis. The CECT is composed of a parallel convolutional encoder block, an aggregate transposed-convolutional decoder block, and a windowed attention classification block. Each block captures features at different scales from 28 $\times$ 28 to 224 $\times$ 224 from the input, composing enriched and comprehensive information. Different from existing methods, our CECT can capture features at both multi-local and global scales without any sophisticated module design. Moreover, the contribution of local features at different scales can be controlled with the proposed ensemble coefficients. We evaluate CECT on two public COVID-19 datasets and it reaches the highest accuracy of 98.1% in the intra-dataset evaluation, outperforming existing state-of-the-art methods. Moreover, the developed CECT achieves an accuracy of 90.9% on the unseen dataset in the inter-dataset evaluation, showing extraordinary generalization ability. With remarkable feature capture ability and generalization ability, we believe CECT can be extended to other medical scenarios as a powerful diagnosis tool. Code is available at https://github.com/NUS-Tim/CECT.