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BioMedInformatics, Volume 5, Issue 3

September 2025 - 23 articles

Cover Story: Medical image classification has become essential for automated disease detection, particularly in gastrointestinal endoscopy where accurate diagnosis impacts patient outcomes. Traditional deep learning approaches, while effective, face computational constraints in clinical deployment. Quantum machine learning offers potential solutions thanks to quantum properties like superposition and entanglement for enhanced computational efficiency. This study introduces the Fused Quantum Dual-Backbone Network, a hybrid framework designed for NISQ-era hardware. Experimental validation demonstrated 95.42% accuracy with 94.44% reduction in trainable parameters versus classical methods. These results indicate that quantum-enhanced architectures can address computational limitations while maintaining diagnostic accuracy for clinical applications. View this paper
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Articles (23)

  • Article
  • Open Access
932 Views
15 Pages

Background: The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deplo...

  • Article
  • Open Access
1,499 Views
21 Pages

This paper investigates the state of substance use disorder (SUD) and the frequency of substance use by utilizing three unsupervised machine learning techniques, based on the Diagnostic and Statistical Manual 5 (DSM-5) of mental health disorders. We...

  • Article
  • Open Access
2,050 Views
23 Pages

High-Precision, Automatic, and Fast Segmentation Method of Hepatic Vessels and Liver Tumors from CT Images Using a Fusion Decision-Based Stacking Deep Learning Model

  • Mamoun Qjidaa,
  • Anass Benfares,
  • Mohammed Amine El Azami El Hassani,
  • Amine Benkabbou,
  • Amine Souadka,
  • Anass Majbar,
  • Zakaria El Moatassim,
  • Maroua Oumlaz,
  • Oumayma Lahnaoui and
  • Raouf Mouhcine
  • + 2 authors

Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due...

  • Article
  • Open Access
2,068 Views
13 Pages

Background: Pediatric Intensive Care Unit (PICU) outcome prediction is challenging, and machine learning (ML) can enhance it by leveraging large datasets. Methods: We built an ML model to predict PICU outcomes (“Death vs. Survival”, &ldqu...

  • Article
  • Open Access
1,040 Views
25 Pages

Quantum-Enhanced Dual-Backbone Architecture for Accurate Gastrointestinal Disease Detection Using Endoscopic Imaging

  • Nabil Marzoug,
  • Khidhr Halab,
  • Othmane El Meslouhi,
  • Zouhair Elamrani Abou Elassad and
  • Moulay A. Akhloufi

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inhere...

  • Article
  • Open Access
2,523 Views
18 Pages

Background: Virtual coaching can help people adopt new healthful behaviors by encouraging them to set specific goals and helping them review their progress. One challenge in creating such systems is analyzing clients’ statements about their act...

  • Article
  • Open Access
1,753 Views
17 Pages

Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study

  • Fareed Ud Din,
  • Nabaraj Giri,
  • Namrata Shetty,
  • Tom Hilton,
  • Niusha Shafiabady and
  • Phillip J. Tully

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in under...

  • Review
  • Open Access
2,751 Views
17 Pages

Real-Time Applications of Biophysiological Markers in Virtual-Reality Exposure Therapy: A Systematic Review

  • Marie-Jeanne Fradette,
  • Julie Azrak,
  • Florence Cousineau,
  • Marie Désilets and
  • Alexandre Dumais

Virtual-reality exposure therapy (VRET) is an emerging treatment for psychiatric disorders that enables immersive and controlled exposure to anxiety-provoking stimuli. Recent developments integrate real-time physiological monitoring, including heart...

  • Article
  • Open Access
1,506 Views
21 Pages

Stabilizing the Shield: C-Terminal Tail Mutation of HMPV F Protein for Enhanced Vaccine Design

  • Reetesh Kumar,
  • Subhomoi Borkotoky,
  • Rohan Gupta,
  • Jyoti Gupta,
  • Somnath Maji,
  • Savitri Tiwari,
  • Rajeev K. Tyagi and
  • Baldo Oliva

Background: Human Metapneumovirus (HMPV) is a respiratory virus in the Pneumoviridae family. HMPV is an enveloped, negative-sense RNA virus encoding three surface proteins: SH, G, and F. The highly immunogenic fusion (F) protein is essential for vira...

  • Review
  • Open Access
3 Citations
7,924 Views
70 Pages

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches

  • Md. Atiqur Rahman,
  • M. Saddam Hossain Khan,
  • Yutaka Watanobe,
  • Jarin Tasnim Prioty,
  • Tasfia Tahsin Annita,
  • Samura Rahman,
  • Md. Shakil Hossain,
  • Saddit Ahmed Aitijjo,
  • Rafsun Islam Taskin and
  • Victor Dhrubo
  • + 2 authors

Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and ris...

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BioMedInformatics - ISSN 2673-7426