Explainable Multi-Backbone Deep Learning with Attention-Based Feature Fusion Framework for Laryngeal Carcinoma Classification using Medical Imaging

Background

Automated diagnostic classification of laryngeal carcinoma using laryngoscopic endoscopic images can ease the workload of otolaryngologists, thereby enhancing their efficiency in practice and supporting young clinicians in achieving accurate diagnoses. Deep Learning has become an effective approach for medical image classification and relies on large labeled datasets. However, several existing methods for laryngeal carcinoma analysis often struggle to capture local and global dependencies, thereby affecting classification accuracy and reliability.