CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features

Purpose: Most computer vision models are developed based on either convolutional neural network (CNN) or transformer, while the former (latter) method captures local (global) features. To relieve model performance limitations due to the lack of global (local) features, we develop a novel classification network named CECT by controllable ensemble CNN and transformer. Methods: The proposed CECT is composed of a CNN-based encoder block, a deconvolution-ensemble decoder block, and a transformer-based classification block. Different from conventional CNN- or transformer-based methods, our CECT can capture features at both multi-local and global scales, and the contribution of local features at different scales can be controlled with the proposed ensemble coefficients. Results: We evaluate CECT on two public COVID-19 datasets and it outperforms other state-of-the-art methods on all evaluation metrics. Conclusion: With remarkable feature capture ability, we believe CECT can also be used in other medical image classification scenarios to assist the diagnosis.