Deep Learning-Enhanced Hyperspectral Imaging

Hyperspectral Imaging (HSI) allows non-invasive, label-free, and highly accurate cancer diagnosis and tumor margin identification. By acquiring and analyzing detailed spectral and spatial information, HSI surpasses the capabilities of traditional imaging modalities. HSI is a robust tool for malignancy detection across diverse oncology domains, including head and neck, skin, and breast cancers. Deep learning (DL) for hyperspectral imaging demonstrates robust, high-accuracy performance for cancer diagnosis and tumor margin identification across oncology. Clinical validation studies frequently report better diagnostic accuracies, sensitivities, and specificities. The learning algorithms such as CNN and LDA attain the highest published metrics for real-time intraoperative and ex vivo assessment. DL applications for white blood cell classification and retinal imaging for diagnosing cardiovascular and metabolic diseases, illustrate the broader utility of these computational imaging approaches in healthcare.