New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 451

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, DK-8200 Aarhus, Denmark
Interests: time series analysis; signal processing; machine learning; medical image and signal processing
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Guest Editor
Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan 4402, Republic of Korea
Interests: fault detection and diagnosis; signal processing; multiscale signal analysis; statistical and temporal signal analysis; signal to image conversion and analysis; artificial intelligence; explainable machine learning; feature engineering; big data; anomaly detection; pattern recognition; algorithms; data structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetic principles in signal and image processing techniques for biomedical applications represent a promising field that draws inspiration from nature to enhance human understanding and the manipulation of biological data. These approaches utilize the principles of natural systems such as neural networks, genetic algorithms, and swarm intelligence to process signals and images with increased efficiency and accuracy.

Recent years have witnessed a growing interest in utilizing biomimetic principles in signal and image processing techniques for advancing medical diagnostics, personalized medicine, and therapeutic interventions, ultimately contributing to better patient care and outcomes.

This Special Issue aims to explore the latest advances in biomimetic approaches applied to signal and image processing within the realm of biomedical research. The scope of this Special Issue encompasses a wide range of topics including, but not limited to, bio-inspired algorithms for signal and image processing; bio-inspired intelligent techniques for medical image classification, detection, localization, and segmentation; biomimetic sensor design and signal processing for medical diagnostics; medical signal processing; medical image processing; and bio-inspired artificial intelligence for signal and image analysis. Furthermore, review articles and research concerning recent advances in bio-inspired techniques for signal and image processing are also encouraged.

Prof. Dr. Jong-Myon Kim
Dr. Naveed Rehman
Dr. Zahoor Ahmad
Guest Editors

Manuscript Submission Information

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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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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

  • signal processing
  • medical signal (e.g., EEG, ECG) processing
  • image processing
  • medical image (e.g., CT, MRI, ultrasound) processing
  • nature-inspired algorithms
  • bio-inspired algorithms
  • artificial intelligence
  • deep learning
  • bio-inspired intelligent algorithms
  • biomimetic sensor design

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Published Papers (1 paper)

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Research

19 pages, 844 KiB  
Article
Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering
by Qingdu Li, Keting Fu, Jian Liu, Yishan Li, Qinze Ren, Kang Xu, Junxiu Fu, Na Liu and Ye Yuan
Biomimetics 2025, 10(5), 296; https://doi.org/10.3390/biomimetics10050296 - 8 May 2025
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Abstract
While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that [...] Read more.
While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article
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