ArticleSensors (Basel, Switzerland)2026
Decision-Aware Vision Mamba with Context-Guided Slot Mixing for Chest X-Ray Screening and Culture-Based Hierarchical Tuberculosis Classification.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Distinguishing Active from Inactive Tuberculosis (TB) on Chest X-rays presents a clinical challenge due to overlapping radiological signs. This study introduces Vision Mamba CGSM, a deep learning framework integrating a State Space Model (SSM) backbone with a Context-Guided Slot Mixing (CGSM) module. The SSM captures global anatomical context, while the CGSM module isolates subtle pathological features by applying localized spatial attention. We validated the model using a hierarchical diagnostic scheme covering Normal, Pneumonia, Active TB, and Inactive TB. Experimental evaluations demonstrate an accuracy of 92.96% and a Youden Index of 79.55% on the independent test set. In the specific binary classification of Active vs. Inactive TB, the model recorded a specificity of 97.04%, outperforming standard baseline architectures including ResNet152 and ViT-B. Additional validations on external datasets confirm the consistent generalization of the proposed feature extraction mechanism.
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