Autoencoder-Integrated WideResNet with Dynamic Optimization (AIW-DynOpt): A Novel Hybrid Deep Learning Approach for Head and Neck Cancer Gene Expression Analysis

Head and neck cancer presents a significant global health challenge, necessitating the development of robust computational models for accurate gene expression analysis. This study introduces Autoencoder-Integrated WideResNet with Dynamic Optimization (AIW-DynOpt), a novel hybrid deep learning framework specifically designed for analyzing head and neck cancer gene expression data. AIW-DynOpt integrates a Deep Undercomplete Autoencoder (DUAE) with a Wide Residual Network (WideResNet) architecture and employs the Successive Halving algorithm for optimal model selection. Utilizing the Cancer Genome Atlas (TCGA) HNSC dataset, which comprises 20,503 genes and 564 samples, our approach focuses on enhancing predictive performance and computational efficiency. A comprehensive evaluation of AIW-DynOpt was conducted, benchmarking it against alternative methods such as DUAE paired with Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Naive Bayes classifiers. The results demonstrate that AIW-DynOpt consistently outperforms the alternative methods across multiple performance metrics, including accuracy, recall, sensitivity, specificity, and Area under the Curve (AUC). Additionally, AIW-DynOpt exhibits superior computational efficiency, significantly reducing model training time while maintaining high predictive accuracy. This study underscores the potential of hybrid deep learning frameworks in advancing computational models for cancer research, positioning AIW-DynOpt as a promising tool for precise gene expression analysis in head and neck cancer and beyond.