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ResNet and its application to medical image processing: Research progress and challenges.

Wanni Xu1, You-Lei Fu2, Dongmei Zhu3

  • 1Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361021, China.

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Summary

Residual neural networks (ResNet) show great promise in interpreting medical images for diagnosing serious illnesses like lung tumors and breast cancer. This review highlights ResNet

Keywords:
Disease diagnosisMedical imageResidual connectionResidual neural network (ResNet)Residual units

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computer Vision
  • Medical Imaging Analysis

Background:

  • Deep learning, a subset of machine learning, is increasingly utilized in computer vision.
  • Residual neural networks (ResNet) have significantly advanced medical image interpretation.
  • ResNet's ability to interpret complex medical images is a key area of research.

Purpose of the Study:

  • To review the current research status of ResNet in the medical field.
  • To explain the fundamental concepts and architecture of ResNet.
  • To discuss the applications, challenges, and future directions of ResNet in medical image processing.

Main Methods:

  • Introduction to the research status and fundamental concepts of ResNet.
  • Explanation of residual units, structures, and network architecture.
  • Discussion of ResNet applications in medical image processing for various diseases.

Main Results:

  • ResNet has demonstrated success in the auxiliary diagnosis of serious illnesses.
  • Applications include processing medical images for lung tumors, skin diseases, breast diseases, and brain diseases.
  • Significant strides have been made in clinical auxiliary diagnosis using ResNet.

Conclusions:

  • ResNet research in medical image processing is a crucial reference point.
  • Summarizes application status and challenges of ResNet in medical image processing.
  • Provides future development directions for ResNet in medical image analysis.