Assessment of Anatomical Changes in Advanced Chronic Venous Insufficiency Using Artificial Intelligence ) and Machine Learning Techniques

Atreyapurapu, Viswanath; Vyakaranam, Malini; Atreya, Srinivas; Gupta, Premchand; Atturu, Gnaneswar

doi:10.1016/j.jvsv.2021.12.059

Cited by 4 publications

(3 citation statements)

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“…The neural network achieved 97% accuracy in distinguishing between normal and abnormal images. Pixel count and energy and dissimilarity ratios were used by the model to objectively quantify the changes to the soft tissues 21 …”

Section: Related Workmentioning

confidence: 99%

“…Pixel count and energy and dissimilarity ratios were used by the model to objectively quantify the changes to the soft tissues. 21 The studies related to the detection and classification of CVI condition in lower limbs using DL techniques were limited in the literature. Therefore, we have summarized some selected studies related to venous disorders and AI approaches using different imaging modalities as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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

Krishnan,

Muthu

2023

Int J Imaging Syst Tech

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Krishnan,

Muthu

2023

Int J Imaging Syst Tech

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“…In addition, this work demonstrates the result of working on a problem of various popular machine learning algorithms (for example, k-means) and making comparative analysis with them. In the work [8], a binary classification problem was solved to determine whether the chronic venous insufficiency exist or not. Authors studied 50 patients and their 5200 images from magnetic resonance venograms.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Barulina¹,

Sanbaev²,

Okunkov³

et al. 2022

Preprint

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Chronic venous disease (CVD) occurs in a substantial proportion of the world's population. If the onset of CVD looks like a cosmetic defect, then over time, it can develop into serious problems that require surgical intervention. The aim of the work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The required for DL algorithms images of legs with CVD were obtained by using Internet Data Mining. For images preprocessing, the binary classification problem “legs - no legs” was solved based on Resnet50 with accuracy 0.998. The application of this filter made it possible to collect a data set of 11,118 good quality leg images with various stages of CVD. For classification of various stages of CVD according to CEAP classification, the multi classification problem was set and resolved by using two neural networks with completely different architecture - Resnet50 and DeiT. The model based on DeiT without any tuning shows better results than the model based on Resnet50 (precision = 0.770 (DeiT) and 0.615 (Resnet50)). To demonstrate the results of the work, a telegram bot was developed, in which fully functioning DL algorithms are implemented. This bot allows evaluating the condition of the patient's legs with a fairly good accuracy for the CVD classification.

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Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Barulina

Sanbaev²,

Okunkov

et al. 2022

Mathematics

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Chronic venous disease (CVD) occurs in a substantial proportion of the world’s population. If the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious problems that will require surgical intervention. The aim of this work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The images of legs with CVD required for DL algorithms were collected from open Internet resources using the developed algorithms. For image preprocessing, the binary classification problem “legs–no legs” was solved based on Resnet50 with accuracy of 0.998. The application of this filter made it possible to collect a dataset of 11,118 good-quality leg images with various stages of CVD. For classification of various stages of CVD according to the CEAP classification, the multi-classification problem was set and resolved by using four neural networks with completely different architectures: Resnet50 and transformers such as data-efficient image transformers (DeiT) and a custom vision transformer (vit-base-patch16-224 and vit-base-patch16-384). The model based on DeiT without any tuning showed better results than the model based on Resnet50 did (precision = 0.770 (DeiT) and 0.615 (Resnet50)). vit-base-patch16-384 showed the best results (precision = 0.79). To demonstrate the results of the work, a Telegram bot was developed, in which fully functioning DL algorithms were implemented. This bot allowed evaluating the condition of the patient’s legs with fairly good accuracy of CVD classification.

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Assessment of Anatomical Changes in Advanced Chronic Venous Insufficiency Using Artificial Intelligence ) and Machine Learning Techniques

Cited by 4 publications

References 0 publications

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

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

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

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