CTBViT: A novel ViT for tuberculosis classification with efficient block and randomized classifier

Abstract

• We propose a new model, CTBViT, for the more efficient and accurate classification of TB in both CT and X-ray images. • We introduce a new Patch Reduction Block (PRB) to improve efficiency by removing unimportant tokens. • We propose a randomized classifier to avoid the overfitting problem encountered when applying large pre-trained models to TB datasets. • We evaluate our model on multiple datasets for testing to verify its generalization. The results show that our model achieves superior performance to existing state-of-the-art techniques. Despite the impressive achievements of Transformer models in tuberculosis (TB) analysis, there are still some shortcomings, such as the high computational cost and overfitting problem. To address these issues, this paper proposes a novel CTBViT model to detect TB in CT and X-ray images. In the traditional transformer block, all the tokens are taken into account for self-attention. However, many regions in the images are irrelevant to the classification task. Therefore, we propose the Patch Reduction Block (PRB) to reduce computation by evaluating each token then dropping less important tokens and retaining only important tokens for subsequent processing. Additionally, we propose the randomized classifier where all the parameters in the input layer are randomly set and do not change during training. This design effectively avoids the overfitting problem. To fairly validate our model, we conducted experiments on both private and public datasets and the CTBViT surpassed the classification performance of other state-of-the-art methods. The results reveal the effectiveness and efficiency of the proposed CTBViT in TB classification, which can be a useful tool in clinical diagnosis.