LGH-Transformer: A Hybrid CNN–Transformer Model for Multi-Class Bladder Tissue Classification from Cystoscopic Images
International Journal of Molecular Sciences, cilt.27, sa.19, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27 Sayı: 19
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/ijms27198929
- Dergi Adı: International Journal of Molecular Sciences
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: bladder cancer, deep learning, image classification, transfer learning, transformer models
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
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 (n = 189). Images were resized to 224 × 224 pixels, normalized, and augmented only in the training subset. Adam and RMSprop were evaluated. Accuracy and macro-averaged precision, recall, and F1-score were calculated from the integer-count evaluation matrices. The highest held-out accuracy, 187/189 (0.9894), was reached by seven model–optimizer configurations: DenseNet201–RMSprop, XCiT Medium 24 P16–Adam, ConvNeXt Base–Adam, CoAtNet–Adam, ConvNeXt Base–RMSprop, CoAtNet–RMSprop, and the proposed LGH-Transformer–RMSprop; MobileNet–Adam and Xcep-tion–RMSprop each yielded 185/189 (0.9788), and LGH-Transformer–Adam yielded 186/189 (0.9841). On a 189-frame test set, differences in one to two frames do not establish statistical superiority, so these configurations are reported as a top-accuracy tier rather than as a ranked superiority claim. Within this tier, the LGH-Transformer had the smallest computational footprint (≈18.2 M parameters and ≈4.1 GFLOPs, versus 20.0–88.6 M parameters and 4.3–16.2 GFLOPs for the other top-tier models). Component-wise ablation experiments showed accuracy decreases of 0.0106, 0.0053, and 0.0053 after removing the CNN tokenizer, local attention, and global attention, respectively. The LGH-Transformer reaches the top accuracy tier of this 18-architecture benchmark at the lowest parameter and FLOP budget within that tier, supporting an internally consistent, efficiency-oriented comparison within the available cohort. The absence of task-aligned external validation and the small number of held-out patients require the findings to be interpreted as exploratory internal validation rather than evidence of clinical generalizability.