CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

Abstract

Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease.