Deep Learning Applications in Stroke Segmentation: Progress, Challenges, and Future Prospects

DOI

10.2991/978-94-6463-540-9_59How to use a DOI?

Keywords

Deep learning; stroke segmentation; artificial intelligence

Abstract

Stroke is a major global health challenge, significantly contributing to disability and death worldwide. Due to the rapid progress of deep learning, the challenges in this field have the potential to be solved. This article offers a comprehensive examination of the uses of deep learning in stroke segmentation. It specifically highlights advanced models like U-Net, RCNN, and their variations. These techniques employ Convolutional Neural Networks (CNNs) to accurately and efficiently segment stroke lesions in medical pictures by classifying each pixel. Research findings suggest that there has been substantial improvement in both precision and speed, providing swift and precise outcomes for stroke detection. Nevertheless, the study also uncovers the difficulties and constraints of current techniques in managing intricate lesions, instantaneous applications, generalization capability, and model interpretability. Subsequent investigations should focus on overcoming these obstacles by investigating intricate network structures, refining optimization methods, developing streamlined models, and implementing visualization explanation approaches. This will significantly enhance the progress of stroke segmentation technology based on deep learning.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xingyi Rong
PY  - 2024
DA  - 2024/10/16
TI  - Deep Learning Applications in Stroke Segmentation: Progress, Challenges, and Future Prospects
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 590
EP  - 596
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_59
DO  - 10.2991/978-94-6463-540-9_59
ID  - Rong2024
ER  -