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Algorithms
Special Issues
Computational, Emerging, and Intelligent Algorithms for Biomedical and Healthcare Applications
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Computational, Emerging, and Intelligent Algorithms for Biomedical and Healthcare Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1731

Special Issue Editors

Prof. Dr. Omneya A. Attallah

E-Mail Website
Guest Editor
Biosignal Processing and Artificial Intelligence in Medicine and Healthcare, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria P.O. Box 1029, Egypt
Interests: artificial intelligence; signal processing; image processing; biomedical signal and image processing; computer vision; pattern recognition; biomedical engineering; machine learning and deep learning; data mining; feature selection; wearable sensors; brain–computer interfaces; neuroinformatics; medical and health informatics; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adel S. Elmaghraby

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: network and information forensics; biomedical imaging; multimedia and virtual reality systems; artificial intelligence; performance evaluation; computer modeling and simulation; human–machine systems; logistics; automation and manufacturing; distributed systems; bioinformatics applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of computational intelligence and biomedical engineering is driving transformative innovations in healthcare. This Special Issue invites high-quality research and comprehensive reviews addressing the development, optimization, and implementation of algorithmic solutions across all areas of biomedicine and healthcare.

We welcome contributions covering both traditional algorithmic approaches—including optimization, statistical learning, and signal processing—and modern paradigms such as machine learning, deep learning, and hybrid intelligent systems. Areas of interest include artificial intelligence, including machine/deep learning in healthcare applications, biomedical signal and image processing, genomics, proteomics, metabolomics, and multi-omics data integration, as well as wearable and sensor data analysis, medical robotics, assistive technologies, biosensing, telehealth, personalized medicine, and immersive technologies (VR/AR/XR) for healthcare and rehabilitation.

This Special Issue particularly encourages interdisciplinary and multidisciplinary studies bridging computer science, engineering, and medical sciences. Submissions may address theoretical advancements, computational models, and experimental validations involving real biomedical datasets or practical healthcare systems. By bringing together diverse methodologies and application domains, this Special Issue aims to advance the frontiers of algorithmic innovation in medicine, fostering intelligent, efficient, and human-centered technologies for improved clinical outcomes and patient care.

We invite researchers to contribute to this Special Issue. Original research articles and reviews are welcome. Topics of interest include, but are not limited to:

  • Biomedical algorithms;
  • Artificial intelligence and deep learning;
  • Signal and image processing;
  • Bioinformatics and multi-omics;
  • Genomics, proteomics, and metabolomics;
  • Wearable and sensor data analysis;
  • Medical robotics and assistive technologies;
  • Computational intelligence in healthcare;
  • Biosensing, telehealth, and rehabilitation;
  • Optimization and statistical modeling;
  • Biomedical engineering applications;
  • Medical imaging and sensing;
  • Immersive technologies (VR/AR/XR) for healthcare and rehabilitation.

We look forward to receiving your contributions.

Prof. Dr. Omneya Attallah
Prof. Dr. Adel S. Elmaghraby
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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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 1800 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

  • biomedical algorithms
  • artificial intelligence and deep learning
  • signal and image processing
  • bioinformatics and multi-omics
  • genomics, proteomics, and metabolomics
  • wearable and sensor data analysis
  • medical robotics and assistive technologies
  • computational intelligence in healthcare
  • biosensing, telehealth, and rehabilitation
  • optimization and statistical modeling
  • biomedical engineering applications
  • medical imaging and sensing
  • immersive technologies (VR/AR/XR) for healthcare and rehabilitation

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

Published Papers (2 papers)

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35 pages, 12420 KB  
Article
LUMINA-Net: Acute Lymphocytic Leukemia Subtype Classification via Interpretable Convolution Neural Network Based on Wavelet and Attention Mechanisms
by Omneya Attallah
Algorithms 2026, 19(4), 298; https://doi.org/10.3390/a19040298 - 10 Apr 2026
Viewed by 295
Abstract
Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such [...] Read more.
Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such as a dependence on solely spatial feature depictions, elevated feature dimensions, computationally extensive deep learning architectures, inadequate multi-layer feature utilization, and poor interpretability. This paper introduces LUMINA-Net, a custom, lightweight, and interpretable deep learning CAD for the automated identification and subtype diagnosis of ALL using microscopic blood smear pictures. LUMINA-Net makes four principal contributions: first, it integrates a self-attention module within a lightweight custom Convolution Neural Network (CNN) to effectively capture long-range spatial relationships across clinically pertinent cytological patterns while preserving a compact design. Second, it employs a Discrete Wavelet Transform (DWT)-based wavelet pooling layer that decreases feature dimensions by up to 96.875% while enhancing the obtained depictions with spatial-spectral information. Third, it utilizes a multi-layer feature fusion strategy that combines wavelet-pooled features from two deep layers with a third fully connected layer to create a discriminating multi-scale feature vector. Fourth, it incorporates Gradient-weighted Class Activation Mapping as a dedicated explainability process to furnish clinicians with apparent visual explanations for each classification decision. Withoit the need for image enhancement or segmentation preprocessing, LUMINA-Net outperforms the competing state-of-the-art methods on the same dataset, achieving a peak accuracy of 99.51%, specificity of 99.84%, and sensitivity of 99.51% on the publicly available Kaggle ALL dataset. This demonstrates that LUMINA-Net has the potential to be a dependable, effective, and clinically interpretable CAD tool for ALL diagnosis. Full article
(This article belongs to the Special Issue Computational, Emerging, and Intelligent Algorithms for Biomedical and Healthcare Applications)
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17 pages, 1253 KB  
Article
ER-ACO: A Real-Time Ant Colony Optimization Framework for Emergency Medical Services Routing and Hospital Resource Scheduling
by Ahmed Métwalli, Fares Fathy, Esraa Khatab and Omar Shalash
Algorithms 2026, 19(2), 102; https://doi.org/10.3390/a19020102 - 28 Jan 2026
Viewed by 869
Abstract
Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an [...] Read more.
Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration (γ=1) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling. Full article
(This article belongs to the Special Issue Computational, Emerging, and Intelligent Algorithms for Biomedical and Healthcare Applications)
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