GEXPNET: a Novel Gene EXPression NETwork for Tumor Classification Using the ResNet-based Deep Learning Approach

Accurate classification of cancer subtypes from mi croarray gene expression data remains challenging due to high dimensionality, class imbalance, and limited sample sizes. To address these issues, we propose GexpNet, a novel deep learning architecture specifically designed for gene expression analysis. GexpNet integrates an initial convolutional layer with dynamic multi-head residual blocks that employ heterogeneous kernel sizes and activation functions. These are followed by an adaptive dense classifier, enabling effective multi-scale feature extraction and robust classification. To reduce noise and overfitting, a customized preprocessing pipeline is introduced, comprising me dian imputation, quantile normalization, and supervised feature selection based on logistic regression. Evaluated on four publicly available datasets, GexpNet achieves 98.99% accuracy (95% CI: 98.57-99.43%) and 98.26% F1-score (95% CI: 97.51-99.01%) on the Mendeley dataset, outperforming prior deep learning models by up to 1.99%. Across three additional datasets, GexpNet con sistently exceeds 99% accuracy with minimal variance in 10-fold cross-validation, confirming its robustness and generalizability. Overall, these results highlight GexpNet as a robust, scalable, and generalizable framework for precision oncology applications.