Chest X-rays (CXRs) are widely utilized for screening pulmonary diseases. However, distinguishing between tuberculosis (TB) and pneumonia remains a challenging task, as their radiological features often exhibit significant overlap. This challenge is particularly acute in resource-constrained settings, where rapid and low-cost automated analysis techniques would be especially beneficial. To address this issue, we propose a lightweight deep learning framework with Triple Permuter and Split Multi-Head Self-Attention (LTS-Net), specifically designed for the classification of TB and pneumonia based on CXR images. The model integrates three core components: a multi-level knowledge distillation strategy aimed at transferring useful knowledge from a large teacher model to a compact encoder; a Triple Permuter (TP) module, which enhances spatial feature interactions without significantly increasing the parameter count; and a Split Multi-Head Self-Attention (SMHSA) module, which effectively reduces the computational complexity of the attention mechanism while preserving global contextual information. We evaluated the proposed method on four publicly available datasets: PX, K-4, TB, and SC. Experimental results demonstrate that LTS-Net achieves classification accuracies ranging from 96.47% to 99.95% across these datasets, while excelling at maintaining relatively low model size and computational cost. Collectively, these findings indicate that LTS-Net strikes a practical balance between diagnostic performance and operational efficiency, positioning it as a highly promising technical solution for automated chest X-ray (CXR) analysis in clinical environments with limited computational resources.
- Open Access
- Article
LTS-Net: A Lightweight and Efficient Deep Learning Framework for Automated Tuberculosis and Pneumonia Detection in Chest X-ray Images
Author Information
Received: 03 Mar 2026 | Revised: 30 May 2026 | Accepted: 03 Jun 2026 | Published: 09 Jun 2026
Abstract
Keywords
tuberculosis | pneumonia | chest X-ray | self-attention | computer-aided diagnosis
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