Accuracy Is Not Enough: An Efficiency- and Statistics-Aware Benchmark of ResNet50 and Vision Transformer for Explainable Multi-Class Gastrointestinal Diseases Classification

Akwasi Asare

Automated classification of gastrointestinal (GI) findings in endoscopic images could support early diagnosis, but architectures are often chosen on accuracy alone. We present a controlled, reproducible benchmark of ResNet50 and ViT-B/16 for eight-class GI classification on the public Kvasir dataset (4,000 images), using a stratified split (400 test images) and stratified group 5-fold cross-validation over all images. In cross-validation, ResNet50 and ViT-B/16 reached 92.12% and 91.70% accuracy, and a two one-sided tests (TOST) procedure showed equivalence within a +/-3 percentage-point margin (difference +0.43 points, 90% CI -0.33 to +1.18; p < 0.001). The architectures differed mainly in cost: ResNet50 had 3.6 times fewer parameters and 4.3 times fewer operations, 2.5-fold lower single-image CPU latency (69 vs. 177 ms) and 3.8-fold higher GPU throughput. Cross-validated calibration was similar (expected calibration error 0.021 vs. 0.023). An edge-oriented transformer, MobileViT-S, was most accurate on the test split (95.5%) but lost 9.4 points when an acquisition artefact present in some classes (the ScopeGuide inset) was occluded, whereas ResNet50 was not significantly…