Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection.

Introduction: Multispectral and hyperspectral imaging have emerged as powerful tools in medical diagnostics, particularly in cancer detection, due to their ability to capture rich spectral information beyond human vision. Traditional approaches for cancer detection rely on handcrafted features and conventional machine learning algorithms, which struggle with high-dimensional spectral data, noise interference, and domain adaptation challenges. Deep learning has recently been introduced to address these limitations, yet existing models often lack robust feature extraction, generalization capability, and effective domain adaptation strategies.