Year: 2025
Type: conference-paper
Abstract: Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers.For treatment to be effective, its stages must be accurately and promptly classified.This study offers a deep learningbased framework for classifying CVD stages using medical images, such as limb photos or ultrasound scans.For training and assessment, convolutional neural networks (CNNs) are used in conjunction with pre-trained models like ResNet, VGG, and Efficient Net.Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model's performance.The encouraging findings suggest that deep learning tools can greatly facilitate the diagnosis of CVD and may be integrated into clinical decision support systems for quicker, more precise evaluations.
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W7117296295