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@article{170396,
        author = {Divya R. Solanki and Himanshu R. Dodiya and Mansi H. Chauhan and Mukesh M. Patel},
        title = {Analysis of Varicose Vein Disease Detection using Machine Learning and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {410-414},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170396},
        abstract = {varicose veins are inflamed, swollen, and twisted veins that typically develop in the lower limbs of the human body. Accurate and early detection of this disease is critical for effective management and prevention of progression. The integration of machine learning (ML) and deep learning (DL) techniques offers a promising solution for enhancing the accuracy of varicose vein detection systems. This study explores the application of ML and DL methods for predicting and detecting varicose vein disease using diverse datasets, including patient demographics, clinical data, and medical imaging. Machine learning models such as decision trees, support vector machines (SVMs), random forests, and ensemble techniques are employed for structured data analysis. Experimental results demonstrate that deep learning models outperform traditional machine learning algorithms, particularly when applied to imaging data. CNNs achieve classification accuracies between 85% and 98%.},
        keywords = {CNN, Deep Learning, Machine Learning, Varicose Veins, Varicose disease},
        month = {December},
        }