Application of deep-learning based computer vision in medical image analysis

Research Article
Open access

Application of deep-learning based computer vision in medical image analysis

Lang Zuo 1*
  • 1 University of Liverpool    
  • *corresponding author sglzuo@liverpool.ac.uk
Published on 22 February 2024 | https://doi.org/10.54254/2755-2721/41/20230761
ACE Vol.41
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-307-4
ISBN (Online): 978-1-83558-308-1

Abstract

The analyzing process of medical image has always been a crucial part for disease detection, diagnosis, surgery assisting and drug delivery. With the outbreak of population worldwide and the constant emergence of all types of complicated diseases, doctors nowadays are facing tremendous workloads and having difficulty in making precise diagnoses or performing more assured operations. Many experts have been striving to develop a wide range of techniques based on deep learning to ease doctors’ burden and improve patient’s recovery process or survival chances. This general review paper will mainly focus on providing readers with a brief understanding and knowledge about deep-learning based computer vision applied in modern medical image analysis. Several academic journals or books related to computer vision applications in medical image analysis were selected for review. Despite the advantages and convenience of this technique, this review finds out that potential obstacles still exist and can be overcome or amended in the future.

Keywords:

Computer Vision, Medical Image Analysis, Convolutional Neural Networks, Medical Nanorobots, Drug Delivery

Zuo,L. (2024). Application of deep-learning based computer vision in medical image analysis. Applied and Computational Engineering,41,259-262.
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References

[1]. Jain, S., jagtap, V. and Pise, N. (2015) ‘Computer Aided Melanoma Skin Cancer Detection Using Image Processing’, Procedia Computer Science, 48, pp. 735–740. Available at: https://doi.org/10.1016/j.procs.2015.04.209.

[2]. Cabria, I. and Gondra, I. (2017) ‘MRI segmentation fusion for brain tumor detection’, Information Fusion, 36, pp. 1–9. Available at: https://doi.org/10.1016/j.inffus.2016.10.003.

[3]. Silva, W. et al. (2022) ‘Computer-aided diagnosis through medical image retrieval in radiology’, Scientific Reports, 12(1), p. 20732. Available at: https://doi.org/10.1038/s41598-022-25027-2.

[4]. K, A. et al. (2022) ‘A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction’, Biomedicines, 10(10), p. 2438. Available at: https://doi.org/10.3390/biomedicines10102438.

[5]. Razzak, M.I., Naz, S. and Zaib, A. (2018) ‘Deep Learning for Medical Image Processing: Overview, Challenges and the Future’, in N. Dey, A.S. Ashour, and S. Borra (eds) Classification in BioApps: Automation of Decision Making. Cham: Springer International Publishing (Lecture Notes in Computational Vision and Biomechanics), pp. 323–350. Available at: https://doi.org/10.1007/978-3-319-65981-7_12.

[6]. Lin, Y. et al. (2023) ‘Imaging-Navigated Surgery’, in Z. Liu (ed.) Visualized Medicine: Emerging Techniques and DevelopinFrontiers. Singapore: Springer Nature (Advances in Experimental Medicine and Biology), pp. 87–106. Available at: https://doi.org/10.1007/978-981-32-9902-3_5.

[7]. Tang, X. et al. (2022) ‘Vision-Based Automated Control of Magnetic Microrobots’, Micromachines, 13(2), p. 337. Available at: https://doi.org/10.3390/mi13020337.

[8]. Tang, Y., Qiu, J. and Gao, M. (2022) ‘Fuzzy Medical Computer Vision Image Restoration and Visual Application’, Computational and Mathematical Methods in Medicine, 2022, p. 6454550. Available at: https://doi.org/10.1155/2022/6454550.

[9]. J, O. et al. (2021) ‘What is new in computer vision and artificial intelligence in medical image analysis applications’, Quantitative imaging in medicine and surgery, 11(8). Available at: https://doi.org/10.21037/qims-20-1151.

Cite this article

Zuo,L. (2024). Application of deep-learning based computer vision in medical image analysis. Applied and Computational Engineering,41,259-262.

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 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-307-4(Print) / 978-1-83558-308-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.41
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Jain, S., jagtap, V. and Pise, N. (2015) ‘Computer Aided Melanoma Skin Cancer Detection Using Image Processing’, Procedia Computer Science, 48, pp. 735–740. Available at: https://doi.org/10.1016/j.procs.2015.04.209.

[2]. Cabria, I. and Gondra, I. (2017) ‘MRI segmentation fusion for brain tumor detection’, Information Fusion, 36, pp. 1–9. Available at: https://doi.org/10.1016/j.inffus.2016.10.003.

[3]. Silva, W. et al. (2022) ‘Computer-aided diagnosis through medical image retrieval in radiology’, Scientific Reports, 12(1), p. 20732. Available at: https://doi.org/10.1038/s41598-022-25027-2.

[4]. K, A. et al. (2022) ‘A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction’, Biomedicines, 10(10), p. 2438. Available at: https://doi.org/10.3390/biomedicines10102438.

[5]. Razzak, M.I., Naz, S. and Zaib, A. (2018) ‘Deep Learning for Medical Image Processing: Overview, Challenges and the Future’, in N. Dey, A.S. Ashour, and S. Borra (eds) Classification in BioApps: Automation of Decision Making. Cham: Springer International Publishing (Lecture Notes in Computational Vision and Biomechanics), pp. 323–350. Available at: https://doi.org/10.1007/978-3-319-65981-7_12.

[6]. Lin, Y. et al. (2023) ‘Imaging-Navigated Surgery’, in Z. Liu (ed.) Visualized Medicine: Emerging Techniques and DevelopinFrontiers. Singapore: Springer Nature (Advances in Experimental Medicine and Biology), pp. 87–106. Available at: https://doi.org/10.1007/978-981-32-9902-3_5.

[7]. Tang, X. et al. (2022) ‘Vision-Based Automated Control of Magnetic Microrobots’, Micromachines, 13(2), p. 337. Available at: https://doi.org/10.3390/mi13020337.

[8]. Tang, Y., Qiu, J. and Gao, M. (2022) ‘Fuzzy Medical Computer Vision Image Restoration and Visual Application’, Computational and Mathematical Methods in Medicine, 2022, p. 6454550. Available at: https://doi.org/10.1155/2022/6454550.

[9]. J, O. et al. (2021) ‘What is new in computer vision and artificial intelligence in medical image analysis applications’, Quantitative imaging in medicine and surgery, 11(8). Available at: https://doi.org/10.21037/qims-20-1151.

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