RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classification

Faisal Ahmed

Keywords: Chest X-ray, Vision Transformer, Channel Replication, Tuberculosis, Pneumonia, Medical Image Analysis

Abstract:

Chest X-ray (CXR) imaging remains one of the most widely used diagnostic tools for detecting pulmonary diseases
such as tuberculosis (TB) and pneumonia. Recent advances in deep learning, particularly Vision Transformers (ViTs),
have shown strong potential for automated medical image analysis. However, most ViT architectures are pretrained
on natural images and require three-channel inputs, while CXR scans are inherently grayscale. To address this gap, we
propose RepViT-CXR, a channel replication strategy that adapts single-channel CXR images into a ViT-compatible
format without introducing additional information loss.
We evaluate RepViT-CXR on three benchmark datasets. On the TB-CXR dataset, our method achieved an accuracy
of 99.9% and an AUC of 99.9%, surpassing prior state-of-the-art methods such as Topo-CXR (99.3% accuracy,
99.8% AUC). For the Pediatric Pneumonia dataset, RepViT-CXR obtained 99.0% accuracy, with 99.2% recall,
99.3% precision, and an AUC of 99.0%, outperforming strong baselines including DCNN and VGG16. On the
Shenzhen TB dataset, our approach achieved 91.1% accuracy and an AUC of 91.2%, marking a performance
improvement over previously reported CNN-based methods. These results demonstrate that a simple yet effective
channel replication strategy allows ViTs to fully leverage their representational power on grayscale medical imaging
tasks. RepViT-CXR establishes a new state of the art for TB and pneumonia detection from chest X-rays, showing
strong potential for deployment in real-world clinical screening systems.

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Transfer Learning and Domain Adaptation

Registration Requirement: Yes

Reproducibility: https://github.com/FaisalAhmed77/RepViT-CXR

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 26