TumorMultiNet: A Statistically Validated Hybrid Deep Learning Framework for Brain Tumor MRI Classification and Segmentation on BRISC2025 with Explainable AI

Our contributions are:
(i) A unified two-branch benchmark on BRISC2025.
Thirteen models, seven ImageNet-pretrained classification backbones and six segmentation architectures, are
trained and evaluated under one preprocessing, optimization and evaluation protocol on identical splits, so the
numbers are comparable across models and across both
tasks.
(ii) TumorMultiNet, a hybrid for each task.
TumorMultiNet-Cls fuses EfficientNet-B0 and ResNet50
by weighted soft voting with test-time augmentation;
TumorMultiNet-Seg averages the probability maps of
SegFormer and DeepLabV3+. Members and fusion
weights come from validation data only, with the test
split untouched.
(iii) Evidence on whether hybridizing is worth it. Every
metric carries a bootstrap 95% confidence interval and
a paired test against each constituent (McNemar’s exact
test; Wilcoxon signed-rank), which separates a real gain
from a cosmetic one that point accuracy would hide.
(iv) A deployment-oriented efficiency profile. Parameters,
GFLOPs, latency, checkpoint size and peak memory are
reported for all thirteen models, quantifying what each
hybrid costs to run.
(v) Explainability and failure characterization. GradCAM confirms the classifier attends to tumor tissue, and
an error analysis ties segmentation failures to small and
multifocal lesions and classification errors to anatomically adjacent classes.
(vi) A working web interface. Both branches are wrapped
in a browser tool returning class, confidence and segmentation overlay for an uploaded slice.