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Diagnostics
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Artificial Intelligence in Neuroimaging 2024
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Artificial Intelligence in Neuroimaging 2024

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 529

Special Issue Editors

Dr. Prabal Datta Barua

E-Mail Website
Guest Editor
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: machine learning; artificial intelligence; deep learning; neural networks; data science; information and communication management
Special Issues, Collections and Topics in MDPI journals
Dr. Turker Tuncer

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Interests: image processing; signal processing; data hiding; feature engineering; visual secret sharing
Special Issues, Collections and Topics in MDPI journals
Dr. Sengul Dogan

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
Interests: feature engineering; machine learning; biomedical image and signal processing; pattern recognition; computer forensics; mobile forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Imaging technologies are frequently used in medicine to diagnose disorders. However, there are some human errors in diagnosis. The best way to implement an intelligent assistant for diagnosis is machine learning. Computer vision has improved tremendously, especially following the integration of deep learning networks. In this Special Issue, we plan to publish papers of models that use neuroimaging techniques and artificial intelligence together. We look forward to publishing your high-quality papers in our journal that present contributions of the latest technology in computer vision, deep learning methods or novel methods recommend for the field. Moreover, you can submit articles on signal-processing-based machine learning models to this Special Issue.

Dr. Prabal Datta Barua
Dr. Turker Tuncer
Dr. Sengul Dogan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MR image classification
  • facial expression recognition
  • MR image segmentation
  • EEG signal classification
  • sEMG signal classification

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Published Papers (1 paper)

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21 pages, 2790 KiB  
Article
Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern
by Turker Tuncer, Sengul Dogan, Irem Tasci, Mehmet Baygin, Prabal Datta Barua and U. Rajendra Acharya
Diagnostics 2024, 14(17), 1987; https://doi.org/10.3390/diagnostics14171987 - 8 Sep 2024
Viewed by 432
Abstract
Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect the brain’s functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we [...] Read more.
Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect the brain’s functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Neuroimaging 2024)
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