ArticleInternational journal of molecular sciences2026
LGH-Transformer: A Hybrid CNN-Transformer Model for Multi-Class Bladder Tissue Classification from Cystoscopic Images.
Article in International journal of molecular sciences, 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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Abstract
Automated analysis of cystoscopic images may support consistent characterization of bladder tissue patterns, but reported performance is highly sensitive to patient composition, data partitioning, and evaluation methodology. Objective: This study provides an exploratory comparison of convolutional neural networks, Transformer-based models, and a proposed Local-Global Hybrid Transformer (LGH-Transformer) for four-class bladder tissue image classification. The dataset comprised 1754 frames and used the publicly released 1257/308/189 training-validation-held-out test split. The annotation-defined held-out test composition was HGC = 74, LGC = 53, NST = 37, and NTL = 25 (
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