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Biomimetics
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New Biomimetic Advances in Signal and Image Processing for Biomedical...
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New Biomimetic Advances in Signal and Image Processing for Biomedical Applications

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: closed (15 September 2024) | Viewed by 8018

Special Issue Editors

Prof. Dr. Jong-Myon Kim

E-Mail Website
Guest Editor
School of IT Convergence, University of Ulsan, Ulsan 4402, Republic of Korea
Interests: machine learning; signal processing; image processing; machine fault diagnosis and health prognosis; condition monitoring; deep learning; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Naveed Rehman

E-Mail Website
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
Dr. Zahoor Ahmad

E-Mail Website
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 and 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 manipulation of biological data. These approaches utilize the principles of natural systems such as neural networks, genetic algorithms, swarm intelligence, etc., 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

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. 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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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23 pages, 7449 KiB  
Article
Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases
by Yerken Mirasbekov, Nurduman Aidossov, Aigerim Mashekova, Vasilios Zarikas, Yong Zhao, Eddie Yin Kwee Ng and Anna Midlenko
Biomimetics 2024, 9(10), 609; https://doi.org/10.3390/biomimetics9100609 - 9 Oct 2024
Viewed by 1541
Abstract
Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, [...] Read more.
Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications)
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24 pages, 10077 KiB  
Article
Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks
by Shokoufeh Mounesi Rad and Sebelan Danishvar
Biomimetics 2024, 9(9), 562; https://doi.org/10.3390/biomimetics9090562 - 18 Sep 2024
Viewed by 1130
Abstract
Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain–computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). [...] Read more.
Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain–computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications)
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33 pages, 2134 KiB  
Article
A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition
by Sunil Kumar Prabhakar and Dong-Ok Won
Biomimetics 2024, 9(9), 513; https://doi.org/10.3390/biomimetics9090513 - 26 Aug 2024
Viewed by 626
Abstract
Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely [...] Read more.
Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely implemented in many applications in the human–computer interface, medical, and entertainment fields. In this work, six transforms, namely, the synchrosqueezing transform, fractional Stockwell transform (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet transform (FAWT), chirplet transform, and superlet transform, are initially applied to speech emotion signals. Once the transforms are applied and the features are extracted, the essential features are selected using three techniques: the Overlapping Information Feature Selection (OIFS) technique followed by two biomimetic intelligence-based optimization techniques, namely, Harris Hawks Optimization (HHO) and the Chameleon Swarm Algorithm (CSA). The selected features are then classified with the help of ten basic machine learning classifiers, with special emphasis given to the extreme learning machine (ELM) and twin extreme learning machine (TELM) classifiers. An experiment is conducted on four publicly available datasets, namely, EMOVO, RAVDESS, SAVEE, and Berlin Emo-DB. The best results are obtained as follows: the Chirplet + CSA + TELM combination obtains a classification accuracy of 80.63% on the EMOVO dataset, the FAWT + HHO + TELM combination obtains a classification accuracy of 85.76% on the RAVDESS dataset, the Chirplet + OIFS + TELM combination obtains a classification accuracy of 83.94% on the SAVEE dataset, and, finally, the KSTDIS + CSA + TELM combination obtains a classification accuracy of 89.77% on the Berlin Emo-DB dataset. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications)
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Review

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13 pages, 708 KiB  
Review
Digital Imaging and Artificial Intelligence in Infantile Hemangioma: A Systematic Literature Review
by Nour Mohamed and Tamer Rabie
Biomimetics 2024, 9(11), 663; https://doi.org/10.3390/biomimetics9110663 - 1 Nov 2024
Viewed by 599
Abstract
Infantile hemangioma (IH) is a vascular anomaly observed in newborns, with potential severe complications if left undetected. Consequently, researchers have turned to artificial intelligence (AI) and digital imaging (DI) methods for detection, segmentation, and assessing the treatment response in IH cases. This paper [...] Read more.
Infantile hemangioma (IH) is a vascular anomaly observed in newborns, with potential severe complications if left undetected. Consequently, researchers have turned to artificial intelligence (AI) and digital imaging (DI) methods for detection, segmentation, and assessing the treatment response in IH cases. This paper conducts a systematic literature review (SLR) following the Kitchenham framework to scrutinize the utilization of AI and digital imaging techniques in IH applications. A total of 21 research articles spanning from 2014 to April 2024 were carefully selected and analyzed to address four key research questions: the issues solved in IH using AI and DI, the most-used AI and DI techniques, the best-performing technique in detecting IH, and the limitations and future directions in the various fields of IH. After an extensive review of the selected articles, it was found that 10 of the 21 articles focused on detecting IH, and 15 articles utilized AI. However, the best-performing technique in detecting IH employed DI. Additionally, the SLR offers insights and recommendations into future directions for IH applications. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications)
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Other

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20 pages, 3873 KiB  
Systematic Review
Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review
by Ivana Hartmann Tolić, Marija Habijan, Irena Galić and Emmanuel Karlo Nyarko
Biomimetics 2024, 9(8), 493; https://doi.org/10.3390/biomimetics9080493 - 14 Aug 2024
Viewed by 1166
Abstract
Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide, though it often goes undiagnosed. The early and [...] Read more.
Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide, though it often goes undiagnosed. The early and accurate diagnosis of celiac disease (CD) is critical to preventing severe health complications, with computer-aided diagnostic approaches showing significant promise. However, there is a shortage of review literature that encapsulates the field’s current state and offers a perspective on future advancements. Therefore, this review critically assesses the literature on the role of imaging techniques, biomarker analysis, and computer models in improving CD diagnosis. We highlight the diagnostic strengths of advanced imaging and the non-invasive appeal of biomarker analyses, while also addressing ongoing challenges in standardization and integration into clinical practice. Our analysis stresses the importance of computer-aided diagnostics in fast-tracking the diagnosis of CD, highlighting the necessity for ongoing research to refine these approaches for effective implementation in clinical settings. Future research in the field will focus on standardizing CAD protocols for broader clinical use and exploring the integration of genetic and protein data to enhance early detection and personalize treatment strategies. These advancements promise significant improvements in patient outcomes and broader implications for managing autoimmune diseases. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications)
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