Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

Medical image recognition based on VGGNet19

Yanke Liu 1 , Honglin Wang * 2 , Yizhuo Zhao 3
1 Shanghai University
2 Zhengzhou University
3 Beijing University of Technology

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 86-94
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yanke Liu, Honglin Wang, Yizhuo Zhao. Medical image recognition based on VGGNet19. ACE (2023) Vol. 17: 86-94. DOI: 10.54254/2755-2721/17/20230919.

Abstract

Gastrointestinal diseases are one of the common clinical diseases, which often require medical imaging for diagnosis and treatment. Recently, the development of deep learning technology has promoted the development of medical image recognition, which provides new ideas and methods for the automatic recognition and analysis of medical images. VGGNet19 is a convolutional neural network model that has attracted much attention because of its simple structure, easy training and better recognition effect. For this reason, this paper proposes an improved VGGNet19 model for medical image recognition of gastrointestinal diseases. Specifically, the project adds an additional fully connected layer and Dropout layer on top of the built VGGNet19 to achieve the recognition of medical images of stomach diseases. Extensive experiments on standard medical stomach images show that the proposed method improves the recognition performance to a certain extent.

Keywords

medical image, VGGNet19, gastrointestinal tract recognition

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230919
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated