Aims and Scope
Accurately localizing and recognizing brain tumors is dependent on precise segmentation of the tumor regions. However, due to the problem of imbalanced data and variability in the dimensions, locations, and shapes of tumors, achieving precise segmentation of brain tumor areas remains a challenging task. One approach to enhance the performance of brain tumor segmentation tasks is through the use of pattern recognition and deep learning models. As a result, deep learning models are widely employed to provide a more comprehensive understanding of brain tumors and improve the accuracy of segmentation results. The purpose of this topic, more specifically, was to develop a suitable approach to segment brain tumor parts (enhancing, edema, and core areas) in the presence of imbalance and lack of data, fuzzy borders, and variation in illumination.
Lead Guest Editor
Ramin Ranjbarzadeh Email: ramin.ranjbarzadehkondrood2@mail.dcu.ie
Dublin City University, Ireland
Research Interests: Computer Vision, Deep Learning, Machine Learning, Optimization
Guest Editors
Saeid Jafarzadeh Ghoushchi Email: s.jafarzadeh@uut.ac.ir
Urmia University of Technology, Iran
Research Interests: Multi-Criteria Decision Making, Mathematical Modelling, Risk Assessment, Failure Mode & Effects Analysis
Araz Darba Email: araz.darba@telenet.be
AGP eGlass, Belgium
Research Interests: Deep Learning, Optimization, Machine Learning
Special Issue Information
We invite authors to submit their original research papers, review articles, or critical perspectives to this special issue. Together, let us explore models to overcome the problems of imbalanced data and fuzzy borders for brain tumors segmentation.
The topics of interest include, but are not limited to, the following:
•Deep learning applied to tumor segmentation;
•Application of reinforcement learning in medical image analysis
•Application of optimization methods for neurological problems
•Machine learning methods for imbalanced data problems in medical image processing
•Applying textural descriptor methods to tumor segmentation applications.
•Transfer learning techniques for medical image analysis
Manuscript Submission Information
Submission deadline: 30 June 2024
Submissions that pass pre-check will be reviewed by at least two reviewers of the specific field. Accepted papers will be published on early access first and sent for copy editing and typesetting. Then all papers will be included in the special issue when it is published.
If you have any queries regarding the special issue or other matters, please feel free to contact the editorial office: jcce@bonviewpress.com
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 International License.
pISSN 2810-9570, eISSN 2810-9503 | Published by Bon View Publishing Pte Ltd.
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