Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Marina Barulina (), Askhat Sanbaev, Sergey Okunkov, Ivan Ulitin and Ivan Okoneshnikov
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Marina Barulina: Institute of Precision Mechanics and Control of the Russian Academy of Sciences, 24, ul. Rabochaya, 410028 Saratov, Russia
Askhat Sanbaev: R&D Department, TOO Fle, Office 20, 24, st. Panfilova, Almaty 050000, Kazakhstan
Sergey Okunkov: Institute of Precision Mechanics and Control of the Russian Academy of Sciences, 24, ul. Rabochaya, 410028 Saratov, Russia
Ivan Ulitin: Institute of Precision Mechanics and Control of the Russian Academy of Sciences, 24, ul. Rabochaya, 410028 Saratov, Russia
Ivan Okoneshnikov: R&D Department, TOO Fle, Office 20, 24, st. Panfilova, Almaty 050000, Kazakhstan

Mathematics, 2022, vol. 10, issue 19, 1-19

Abstract: 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.

Keywords: chronic venous disease; deep learning; data mining; Resnet50; ViT; DeiT; automatic classification; automatic CEAP classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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