Design of an iterative physiologically guided hybrid deep learning framework for robust hand vein segmentation, blood flow analysis, and early vascular diagnosis.

Reliable imaging and interpretation methods are necessary for the early and non-invasive diagnosis of vascular disorders, especially for blood flow assessment and vein detection in the human hand. Low-contrast near-infrared (NIR) images, subject-specific anatomical variability, and inadequate physiological integration lead to poor generalization, which are common limitations of current approaches. These limitations make it more difficult to accurately identify minute vascular alterations that are essential for pre-symptomatic monitoring. This paper suggests Bio-TransUNet, a unified deep learning framework that combines disease classification, segmentation, and structural validation, to address these issues. For accurate and reliable vein segmentation, Bio-TransUNet uses a multiscale spatial-temporal attention mechanism. Biophysically regularized learning is then used to increase robustness across different anatomies. Probabilistic graph modeling of vein structures further guarantees anatomical fidelity. Lastly, flow-aware adaptation and physiological priors are used to improve disease classification, allowing for precise diagnosis even in situations with little data. Employs physiological and temporal cues to improve segmentation and generalization. Makes use of probabilistic validation and vein graph modeling to guarantee anatomical consistency. Incorporates transformer-based classification and domain adaptation to provide precise early-stage diagnosis.