Multidimensional Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) for Classification of Varicose Veins and Associated Chronic Diseases

Varicose veins (VV) and chronic venous diseases (CVD) affect a significant portion of the global population, often leading to pain, edema, and in advanced cases, ulceration. Accurate classification of disease severity is essential for timely intervention and optimal treatment planning. This study proposes a novel multimodal framework that integrates multidimensional Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) for the automated classification of varicose veins and related chronic conditions. The CNN module is designed to process high-resolution vascular imaging data, including Doppler ultrasound and venography scans, while the LLM component analyzes unstructured clinical text such as physician notes and electronic health records. The fusion of visual and textual features enables robust classification based on the CEAP (Clinical-Etiological-Anatomical-Pathophysiological) system and ICD-10 codes. Experimental results demonstrate that the proposed hybrid model outperforms unimodal approaches in terms of accuracy, sensitivity, and clinical relevance. This research highlights the potential of deep learning-based multimodal systems in enhancing diagnostic precision and decision support in vascular medicine.