Table 5 Comparison of evaluation performance between existing studies and our work. The upper portion presents the performance of individual models, while the lower portion presents results for ensembled methods. ‘–’ denotes results that are not available.

From: A triple pronged approach for ulcerative colitis severity classification using multimodal, meta, and transformer based learning

Method

Accuracy

Precision

Recall

F1

MCC

ResNet509

0.72

0.84

VGG1910

0.74

0.84

InceptionV38

0.84

0.89

DenseNet12111

0.87

0.91

Majority class7

0.72

0.84

Swin-base (ours)

0.90

0.90

0.90

0.89

0.77

DL-ensemble4

0.80

0.83

0.90

0.86

0.47

TL-ensemble4

0.90

0.98

0.89

0.93

0.76

ViT weighted voting ensemble (ours)

0.91

0.91

0.91

0.90

0.76

ViT soft voting ensemble (ours)

0.93

0.94

0.93

0.93

0.77

  1. Significant values are in bold.