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Recent Trends in Artificial Learning and Data Processing for Biomedical...
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Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 13264

Special Issue Editors

Prof. Dr. Larbi Boubchir

E-Mail Website
Guest Editor
LIASD Research Lab., University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Boubaker Daachi

E-Mail Website
Guest Editor
LIASD Research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: robotics; soft computing; BCI; WSN; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to solicit original research papers focusing on novel solutions to challenging problems in biomedical engineering using artificial learning and advanced data processing algorithms and methods.

We are inviting original research works covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the biomedical engineering field.

The main topics of interest include but are not limited to:

  • Biomedical signal processing;
  • Medical and biological imaging;
  • Pattern recognition algorithms and methods;
  • Artificial learning algorithms and methods (e.g., machine learning, deep learning, statistical learning);
  • Applications of artificial intelligence in biomedical engineering;
  • Healthcare applications (e.g., detection, diagnostic, therapeutic, e-health, m-health);
  • Healthcare Internet of Things;
  • Smart Healthcare;
  • Decision support systems in biomedical engineering;
  • Neural engineering;
  • Clinical engineering;
  • Rehabilitation engineering;
  • Biological engineering;
  • Biomedical sensors and devices;
  • Biomedical wearable technology;
  • Related applications.

Prof. Dr. Larbi Boubchir
Prof. Dr. Boubaker Daachi 
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. Electronics 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 2400 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

  • image processing
  • pattern recognition
  • artificial intelligence
  • machine learning
  • feature engineering
  • biomedical engineering
  • healthcare

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Published Papers (6 papers)

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Research

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20 pages, 3455 KiB  
Article
Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection
by Ahmad Ishaq, Fath U Min Ullah, Prince Hamandawana, Da-Jung Cho and Tae-Sun Chung
Electronics 2025, 14(4), 710; https://doi.org/10.3390/electronics14040710 - 12 Feb 2025
Cited by 1 | Viewed by 1256
Abstract
Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have [...] Read more.
Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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19 pages, 688 KiB  
Article
Advancing Pulmonary Nodule Detection with ARSGNet: EfficientNet and Transformer Synergy
by Maroua Oumlaz, Yassine Oumlaz, Aziz Oukaira, Amrou Zyad Benelhaouare and Ahmed Lakhssassi
Electronics 2024, 13(22), 4369; https://doi.org/10.3390/electronics13224369 - 7 Nov 2024
Viewed by 1123
Abstract
Lung cancer, the leading cause of cancer-related deaths globally, presents significant challenges in early detection and diagnosis. The effective analysis of pulmonary medical imaging, particularly computed tomography (CT) scans, is critical in this endeavor. Traditional diagnostic methods, which are manual and time-intensive, underscore [...] Read more.
Lung cancer, the leading cause of cancer-related deaths globally, presents significant challenges in early detection and diagnosis. The effective analysis of pulmonary medical imaging, particularly computed tomography (CT) scans, is critical in this endeavor. Traditional diagnostic methods, which are manual and time-intensive, underscore the need for innovative, efficient, and accurate detection approaches. To address this need, we introduce the Adaptive Range Slice Grouping Network (ARSGNet), a novel deep learning framework that enhances early lung cancer diagnosis through advanced segmentation and classification techniques in CT imaging. ARSGNet synergistically integrates the strengths of EfficientNet and Transformer architectures, leveraging their superior feature extraction and contextual processing capabilities. This hybrid model proficiently handles the complexities of 3D CT images, ensuring precise and reliable lung nodule detection. The algorithm processes CT scans using short slice grouping (SSG) and long slice grouping (LSG) techniques to extract critical features from each slice, culminating in the generation of nodule probabilities and the identification of potential nodular regions. Incorporating shapley additive explanations (SHAP) analysis further enhances model interpretability by highlighting the contributory features. Our extensive experimentation demonstrated a significant improvement in diagnostic accuracy, with training accuracy increasing from 0.9126 to 0.9817. This advancement not only reflects the model’s efficient learning curve but also its high proficiency in accurately classifying a majority of training samples. Given its high accuracy, interpretability, and consistent reduction in training loss, ARSGNet holds substantial potential as a groundbreaking tool for early lung cancer detection and diagnosis. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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24 pages, 6207 KiB  
Article
Dynamic Partitioning of Graphs Based on Multivariate Blood Glucose Data—A Graph Neural Network Model for Diabetes Prediction
by Jianjun Li, Xiaozhe Jiang and Kaiyue Wang
Electronics 2024, 13(18), 3727; https://doi.org/10.3390/electronics13183727 - 20 Sep 2024
Cited by 1 | Viewed by 1337
Abstract
Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at [...] Read more.
Postprandial Hyperglycemia (PPHG) persistently threatens patients’ health. Therefore, accurate diabetes prediction is crucial for effective blood glucose management. Most current methods primarily focus on analyzing univariate blood glucose data using traditional neural networks, neglecting the importance of spatiotemporal modeling of multivariate data at the node and subgraph levels. This study aimed to evaluate the accuracy of using deep learning (DL) techniques to predict diabetes based on multivariable blood glucose data, aiming to improve resource allocation and decision-making in healthcare. We introduce a Nonlinear Aggregated Graph Neural Network (NLAGNN) that utilizes continuous multivariate historical blood glucose data from multiple patients to predict blood glucose levels over time, addressing the challenge of accurately extracting strong and weak correlation features. We preliminarily propose a Nonlinear Fourier Graph Neural Operator (NFGO) for nonlinear node representation, which effectively reduces meaningless noise. Additionally, a dynamic partitioning of graphs is introduced, which divides the a hypergraph into distinct subgraphs, enabling the further processing of strongly correlated features at the node and subgraph levels, ultimately obtaining the final prediction through layer aggregation. Extensive experiments on three datasets show that our proposed method achieves competitive results compared to existing advanced methods. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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22 pages, 577 KiB  
Article
Efficient Human Activity Recognition on Wearable Devices Using Knowledge Distillation Techniques
by Paulo H. N. Gonçalves, Hendrio Bragança and Eduardo Souto
Electronics 2024, 13(18), 3612; https://doi.org/10.3390/electronics13183612 - 11 Sep 2024
Cited by 1 | Viewed by 1642
Abstract
Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but [...] Read more.
Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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15 pages, 5290 KiB  
Article
An Empirical Mode Decomposition-Based Method to Identify Topologically Associated Domains from Chromatin Interactions
by Xuemin Zhao, Ran Duan and Shaowen Yao
Electronics 2023, 12(19), 4154; https://doi.org/10.3390/electronics12194154 - 6 Oct 2023
Cited by 2 | Viewed by 1512
Abstract
Topologically associated domains (TADs) represent essential units constituting chromatin’s intricate three-dimensional spatial organization. TADs are stably present across cell types and species, and their influence on vital biological processes, such as gene expression, DNA replication, and chromosomal translocation, underscores their significance. Accordingly, the [...] Read more.
Topologically associated domains (TADs) represent essential units constituting chromatin’s intricate three-dimensional spatial organization. TADs are stably present across cell types and species, and their influence on vital biological processes, such as gene expression, DNA replication, and chromosomal translocation, underscores their significance. Accordingly, the identification of TADs within the Hi-C interaction matrix is a key point in three-dimensional genomics. TADs manifest as contiguous blocks along the diagonal of the Hi-C interaction matrix, which are characterized by dense interactions within blocks and sparse interactions between blocks. An optimization method is proposed to enhance Hi-C interaction matrix data using the empirical mode decomposition method, which requires no prior knowledge and adaptively decomposes Hi-C data into a sum of multiple eigenmodal functions via exploiting the inherent characteristics of variations in the input Hi-C data. We identify TADs within the optimized data and compared the results with five commonly used TAD detection methods, namely the Directionality Index (DI), Interaction Isolation (IS), HiCKey, HiCDB, and TopDom. The results demonstrate the universality and efficiency of the proposed method, highlighting its potential as a valuable tool in TAD identification. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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29 pages, 1905 KiB  
Systematic Review
Health Risk Assessment Using Machine Learning: Systematic Review
by Stanley Ebhohimhen Abhadiomhen, Emmanuel Onyekachukwu Nzeakor and Kiemute Oyibo
Electronics 2024, 13(22), 4405; https://doi.org/10.3390/electronics13224405 - 11 Nov 2024
Cited by 2 | Viewed by 4244
Abstract
According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general [...] Read more.
According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.’s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions. Full article
(This article belongs to the Special Issue Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering)
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