Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present subtle mucosal changes that are difficult to identify using standard White Light Imaging (WLI), and hardware-based Narrow Band Imaging (NBI) is not universally available. In this study, we propose a novel image processing algorithm termed the Spectrum-Aided Vision Enhancer (SAVE) to address these challenges in computer-aided diagnosis (CAD). Leveraging hyperspectral data principles, SAVE transforms standard WLI endoscopic images into enhanced, NBI-like representations, significantly improving mucosal contrast and lesion visibility without requiring additional hardware. To validate the efficacy of this approach for medical image analysis, we utilized a dataset of Squamous Cell Carcinoma (SCC) and dysplasia. We conducted a comprehensive comparative analysis using five state-of-the-art deep learning models: YOLOv8, InceptionV3, Inception-ResNet-V2, ConvNeXt-V2, and MobileNetV2. Experimental results demonstrate that models trained on SAVE-enhanced images significantly outperform those trained on traditional WLI in both classification and detection tasks. This study presents a cost-effective, software-driven solution that integrates advanced image processing with deep learning, offering a robust tool for the automated screening of esophageal malignancies.