Applications of Deep Learning in Medical Imaging: A Systematic Review

Ruiqi Li

doi:10.47297/wspbdsWSP2752-630528.20250503

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Applications of Deep Learning in Medical Imaging: A Systematic Review

Volume 5, Issue 3

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Applications of Deep Learning in Medical Imaging: A Systematic Review
Applications of Deep Learning in Medical Imaging: A Systematic Review
Zhejiang Chinese Medical University, Medical Information Engineering, Hangzhou, Zhejiang, 310053, China
Vol. 5, Issue 3, Pages: 138-141(2025)
Published：30 September 2025
DOI：10.47297/wspbdsWSP2752-630528.20250503
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Li Ruiqi.Applications of Deep Learning in Medical Imaging: A Systematic Review[J].Biomedical Data Science,2025,05(03):138-141.
DOI：

Li Ruiqi.Applications of Deep Learning in Medical Imaging: A Systematic Review[J].Biomedical Data Science,2025,05(03):138-141. DOI： 10.47297/wspbdsWSP2752-630528.20250503.

摘要

Abstract

With the increasing number of medical images and the continuous progress of artificial intelligence technology

the traditional medical image analysis methods relying on artificial features and experts' experience have been difficult to meet the dual requirements of efficiency and accuracy in modern clinics. In recent years

deep learning has shown strong application potential in the field of medical imaging

and has gradually become an important tool for improving image processing efficiency and diagnosis level. In this paper

from the three target directions of image segmentation

image registration and image enhancement

we systematically sort out the research progress and application of deep learning in medical image analysis

discuss its practical value in clinical practice with the current research cases

summarise the main challenges faced by the current technology

and look forward to the future development direction of deep learning in medical imaging. By collating and analysing the existing research results

it can provide useful references and ideas for the future development of intelligent analysis of medical images.

关键词

Keywords

references

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