TBformer

TBformer is a neural network architecture proposed for tuberculosis (TB) detection from chest X-rays. It is based on the Transformer model, a type of deep learning architecture that has achieved significant success in natural language processing and has been adapted for computer vision tasks. The "TB" in TBformer refers to its specific application in identifying signs of tuberculosis.

The core idea behind TBformer is to leverage the attention mechanisms inherent in the Transformer architecture. These mechanisms allow the model to weigh the importance of different parts of an image when making a prediction. In the context of chest X-rays, this means the model can learn to focus on specific regions or patterns that are indicative of TB, such as opacities, infiltrates, or pleural effusions.

TBformer typically processes an input chest X-ray by dividing it into smaller patches. These patches are then treated as tokens, similar to words in a sentence, and fed into the Transformer encoder. The encoder, through its self-attention layers, learns contextual relationships between these image patches. This enables the model to capture long-range dependencies and complex spatial patterns that might be missed by traditional convolutional neural networks (CNNs). The final output of the TBformer model is a classification indicating the likelihood of TB presence. Research has shown TBformer to achieve competitive or superior performance compared to other deep learning models for TB detection.