8-class classifier (dyed-lifted-polyps, dyed-resection-margins, esophagitis, normal-cecum, normal-pylorus, normal-z-line, polyps, ulcerative-colitis) for GI endoscopy frames, trained on Kvasir-Dataset-v2 with stratified group 10-fold cross-validation.

Architecture

ConvNeXt V2 (tiny) backbone — local/texture features
Swin Transformer (tiny) backbone — global/contextual features
Bidirectional cross-attention fusion (2 layers) between both token streams
MLP classification head

10-Fold Cross-Validation Results (mean ± std)

Metric	Value
Accuracy	0.9514 ± 0.0057
Precision (macro)	0.9520 ± 0.0054
Recall (macro)	0.9514 ± 0.0057
F1 (macro)	0.9513 ± 0.0057
ROC-AUC (macro)	0.9959 ± 0.0014

Usage

python

from huggingface_hub import hf_hub_download
# see inference.py in this repo for load_model() / predict()

Explainability

Grad-CAM visualizations for both the ConvNeXt and Swin streams are included in the original training notebook to support clinical interpretability.

AmimulBmeIU/convnextv2-swin-kvasir-gi-endoscopy

ConvNeXt V2 + Swin Transformer + Cross-Attention Fusion — Kvasir Dataset v2 (8-class GI Endoscopy)

Architecture

10-Fold Cross-Validation Results (mean ± std)

Usage

Explainability

AmimulBmeIU/
convnextv2-swin-kvasir-gi-endoscopy