Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

Version 1 : Received: 26 August 2022 / Approved: 29 August 2022 / Online: 29 August 2022 (12:46:56 CEST)

A peer-reviewed article of this Preprint also exists.

Barulina, M.; Sanbaev, A.; Okunkov, S.; Ulitin, I.; Okoneshnikov, I. Deep Learning Approaches to Automatic Chronic Venous Disease Classification. Mathematics 2022, 10, 3571. Barulina, M.; Sanbaev, A.; Okunkov, S.; Ulitin, I.; Okoneshnikov, I. Deep Learning Approaches to Automatic Chronic Venous Disease Classification. Mathematics 2022, 10, 3571.

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, 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.

Keywords

chronic venous disease; deep leaning; data mining; Resnet50; DeiT; automatic classification; automatic CEAP classification

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.