MDVPB: Design of an Efficient Modified Densenet-161 Model for Advanced Vein Pattern Recognition in Biomedical Systems

Abstract

There is an ever-increasing need for an efficient, and accurate vein detection system that can work in clinical scenarios. Traditional methods, while effective, grapple with challenges like high error rates and susceptibility to environmental variations, making the need for advanced solutions more pressing. This study addresses these limitations by unveiling a groundbreaking Convolutional Neural Network (CNN) architecture, tailored specifically for the nuanced recognition of vein patterns, a critical component in biomedical systems. The existing frameworks in vein pattern recognition often falter in handling intricate variations, leading to diminished accuracy and reliability levels. The proposed model revolutionizes this landscape by integrating a modified Densenet-161 structure for analyzing blood flow velocity and recognizing dorsal hand vein locations. The ingenuity lies in the removal of the conventional classification layer, transforming it into a robust feature embedder. This strategic alteration enables the extraction of remarkably distinct vein features from hand images, a leap forward in capturing the unique identifiers crucial in biomedical systems. The methodology employed is twofold: a meticulous training and validation phase, followed by a testing phase under different verification scenarios. The training harnesses the power of the CNN feature embedder, extracting and classifying features with an unprecedented precision. The testing phase introduces a dual input system, comparing known and unknown vein patterns using Euclidean distance, a method that quantifies similarity with remarkable accuracy levels. This process significantly reduces intra-class variations while amplifying inter-class differences, culminating in a system that is not only highly accurate but also reliable for clinical scenarios. The model's superiority is further evidenced by its performance on various datasets, showcasing improvements in precision, accuracy, recall, AUC, and reduced delay, outperforming existing methods by notable margins. The model can thus be used for intravenous catheterization, blood check-up, and analysis of different veins identified related to different diseases.