TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses

Utilizing electronic noses (e-noses) with pattern recognition
algorithms
offers a promising noninvasive method for the early detection of urinary
bladder cancer (UBC). However, limited clinical samples often hinder
existing artificial intelligence (AI)-assisted diagnosis. This paper
proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis,
leveraging easily obtainable UBC-related volatile organic components
(VOCs) as auxiliary classification categories. These VOCs are biomarkers
of UBC, helping the model learn more UBC-specific features, reducing
overfitting in small sample scenarios, and reflecting the imbalanced
distribution of clinical samples. TC-Sniffer employs intensity-based
augmentation to address small sample size issues and focal loss to
alleviate model bias due to the class imbalance caused by auxiliary
VOCs. The architecture combines transformers and temporal convolutional
neural networks to capture long- and short-range dependencies, achieving
comprehensive representation learning. Additionally, feature-level
constraints further enhance the learning of distinctive features for
each class. Experimental results using e-nose data collected from
a custom-designed sensor array show that TC-Sniffer significantly
surpasses existing approaches, achieving a mean accuracy of 92.95%
with only five UBC training samples. Moreover, the fine-grained classification
results show that the model can distinguish between nonmuscle-invasive
bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC),
both of which are subtypes of UBC. The superior performance of TC-Sniffer
highlights its potential for timely and accurate cancer diagnosis
in challenging clinical settings.