2026 International Conference on Networks Computers and Communications (ICNCCom 2026)

Pathogenic fungi are difficult to identify in the field. The task of reading microscopic slides is still primarily one that requires training from human eyes — a point of delay in clinical
decisions and an introduction of inter-observer error. This paper proposes a new confidence-guided multi-scale fusion framework called ACMF. The framework runs two Vision Transformer (ViT-B/16) encoders at different input resolutions — 224 and
384 pixels — and merges the outputs in four different ways. These range from simple averaging (S1) and accuracy-weighted blending (S2) to a per-image entropy-based weighting scheme (S3) and an expert MLP fusion head (S4). Experiments on the
DeFungi benchmark — five clinically relevant fungal species, 6,801 microscopic images — demonstrate competitive accuracy. The best-performing variant is S2, with a macro F1 of 91.63% on the held-out test set — a 3.16-point gain over ViT-B/16-224 used alone. Attention Rollout maps are generated at inference time, highlighting hyphae and spore structures without additional annotation. Comparisons with ResNet-50, EfficientNet-B3, and Swin-Tiny confirm that ACMF delivers the most consistent value.

fungal classification,vision transformer,deep learning,explainable AI,multi-scale fusion,DeFungi,microscopy