Computational and Mathematical Methods in Medicine

Mathematical Aspects Behind Deep Learning and Transfer Learning Approaches for Medical Image Analysis


Publishing date
01 Oct 2021
Status
Closed
Submission deadline
28 May 2021

Lead Editor

1Karunya University, Coimbatore, India

2Purdue University Fort Wayne, Fort Wayne, USA

3Aurel Vlaicu University of Arad, Arad, Romania

This issue is now closed for submissions.
More articles will be published in the near future.

Mathematical Aspects Behind Deep Learning and Transfer Learning Approaches for Medical Image Analysis

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Computational Intelligence approaches are widely used in practical applications including the medical field. Although many computational approaches are available, few methods such as deep learning techniques are widely preferred for medical image analysis.

Even though deep learning techniques are analysed theoretically for research, their practical feasibility is still unknown due to the large computational complexity associated with such approaches. These computational operations are highly mathematical in nature, hence there is a strong necessity for simplifying these computational processes. With the rise in software facilities, most researchers do not consider the mathematical aspects of these deep learning approaches.

The aim of this Special Issue is to collate original research focussing on the mathematical concepts of deep learning approaches regarding medical image analysis. Review articles discussing the current state of the art are also welcome. We hope that this Special Issue enables budding researchers to further investigate deep learning approaches.

Potential topics include but are not limited to the following:

  • Mathematical modelling of deep learning and transfer learning approaches for medical image analysis
  • Hybridised deep learning approaches for medical image analysis
  • Convolutional neural network for neurological disorders
  • Deep autoencoders for optimization of feature set from medical images
  • Deep feedforward networks for classification of medical images
  • Medical image segmentation using deep learning and transfer learning approaches
  • Generative Adversarial Networks for medical image analysis
  • Hybrid transfer learning approaches for medical image understanding
  • Medical image/video processing for remote monitoring of health data
  • Novel simplified deep learning training algorithms for medical image analysis
Computational and Mathematical Methods in Medicine
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