Medical image recognition based on VGGNet19

Research Article
Open access

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    
  • *corresponding author leowp@stu.zzu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/17/20230919
ACE Vol.17
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-025-7
ISBN (Online): 978-1-83558-026-4

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

Liu,Y.;Wang,H.;Zhao,Y. (2023). Medical image recognition based on VGGNet19. Applied and Computational Engineering,17,86-94.
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References

[1]. Pu, L., Liu, X., Zhang, Y., Li, J., & Li, J. Diagnostic performance of magnetic resonance imaging for detecting perianal fistula and abscess: a systematic review and meta-analysis.2021 PloS one, 16(5), e0251838.

[2]. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J. et al. Dermatologist-level classification of skin cancer with deep neural networks. 2017, Nature, 542(7639), 115–118.

[3]. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? 2021, IEEE Trans. Medi. Imag., 35(5), 1299–1312.

[4]. Wang, X., Zhang, Y., Zhang, J., Sun, X., Chen, H., He, Y., & Hu, Z. Lung cancer screening with low-dose chest computed tomography using three-dimensional deep learning. 2021 J. X-Ray Sci. Tech., 29(1), 1-12.

[5]. Liu, Y., Wang, Y., Zhang, X., Zhang, Q., & Chen, Z. Deep learning-based automatic detection of dental caries in panoramic radiographs. 2020, J. Dentist., 99, 103407.

[6]. Simonyan, K., & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2015, Inter. Conf. Learn. Represent. 1-14.

[7]. Wong, A., et al. Data Augmentation for Deep Learning: A Review. 2018, ACM Computing Surveys, 51(4), 1-36.

[8]. He, K., et al. Deep Residual Learning for Image Recognition. 2016, Conf. Comput. Vis. Patt. Rec., 770-778.

[9]. He, K., Zhang, X., Ren, S., & Sun, J. Deep Residual Learning for Image Recognition. 2016, Conf. Comput. Vis. Patt. Rec. 770-778.

[10]. Buchan, S., et al. Difficulty of acquiring clinical brain MRI data for three machine learning tasks. 2019, Scientific Data, 6(1), 1-7.

Cite this article

Liu,Y.;Wang,H.;Zhao,Y. (2023). Medical image recognition based on VGGNet19. Applied and Computational Engineering,17,86-94.

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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About volume

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

ISBN：978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.17
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Pu, L., Liu, X., Zhang, Y., Li, J., & Li, J. Diagnostic performance of magnetic resonance imaging for detecting perianal fistula and abscess: a systematic review and meta-analysis.2021 PloS one, 16(5), e0251838.

[2]. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J. et al. Dermatologist-level classification of skin cancer with deep neural networks. 2017, Nature, 542(7639), 115–118.

[3]. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? 2021, IEEE Trans. Medi. Imag., 35(5), 1299–1312.

[4]. Wang, X., Zhang, Y., Zhang, J., Sun, X., Chen, H., He, Y., & Hu, Z. Lung cancer screening with low-dose chest computed tomography using three-dimensional deep learning. 2021 J. X-Ray Sci. Tech., 29(1), 1-12.

[5]. Liu, Y., Wang, Y., Zhang, X., Zhang, Q., & Chen, Z. Deep learning-based automatic detection of dental caries in panoramic radiographs. 2020, J. Dentist., 99, 103407.

[6]. Simonyan, K., & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2015, Inter. Conf. Learn. Represent. 1-14.

[7]. Wong, A., et al. Data Augmentation for Deep Learning: A Review. 2018, ACM Computing Surveys, 51(4), 1-36.

[8]. He, K., et al. Deep Residual Learning for Image Recognition. 2016, Conf. Comput. Vis. Patt. Rec., 770-778.

[9]. He, K., Zhang, X., Ren, S., & Sun, J. Deep Residual Learning for Image Recognition. 2016, Conf. Comput. Vis. Patt. Rec. 770-778.

[10]. Buchan, S., et al. Difficulty of acquiring clinical brain MRI data for three machine learning tasks. 2019, Scientific Data, 6(1), 1-7.

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