Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 19, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection.

Narinder Kaur1, Shakir Khan2, Bobbinpreet Kaur1

  • 1Department of Computer Science &; Engineering, Chandigarh University, Mohali, Punjab, India.

Frontiers in Medicine
|May 18, 2026
PubMed
Summary

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same author

An interpretable deep concatenated architecture for osteoporosis detection using enhanced knee radiographs.

Frontiers in medicine·2026
Same author

Miniaturization in robotic surgery: a bibliometric and science mapping analysis of technological evolution and clinical translation (1996-2026).

Journal of robotic surgery·2026
Same author

Scientific growth and research impact in robotic hepatobiliary and pancreatic surgery: evidence from almost 1,000 publications and top- 100 most cited literature.

Journal of robotic surgery·2026
Same author

The landscape of machine learning in clinical applications: A thematic mapping of evolution, frontiers, and future opportunities.

Digital health·2026
Same author

Correction: Comparative evaluation of deep learning models for lung segmentation in chest X-rays: applications in infectious disease screening.

BMC infectious diseases·2026
Same journal

Integrated analysis of the aqueous humor microbiome and lens capsule transcriptome in high myopia cataract: a pilot study.

Frontiers in medicine·2026
Same journal

Internal jugular vein catheterization and catheter insertion slipped into the cervical spinal canal: a special case report.

Frontiers in medicine·2026
Same journal

Prediction models for mortality in patients with acute on chronic liver failure: systematic review and critical appraisal.

Frontiers in medicine·2026
Same journal

Hypertension among Middle Eastern and North African adults residing in the United States: addressing equity in health research representation using the All of Us Research Program, 2000-2024.

Frontiers in medicine·2026
Same journal

Effects of problem-focused versus emotion-focused behavioral interventions on peptic ulcer recurrence: a 4-year retrospective cohort study.

Frontiers in medicine·2026
Same journal

Infections and CAR-T cells for the treatment of lymphoid malignancies: a narrative review.

Frontiers in medicine·2026
See all related articles
This summary is machine-generated.

This study introduces a novel deep learning ensemble model for Acute Lymphoblastic Leukemia (ALL) detection, achieving high accuracy. The FOO-Ensemble model offers a reliable computer-aided diagnostic tool to improve patient outcomes.

Area of Science:

  • Hematology
  • Computer Science
  • Artificial Intelligence

Background:

  • Acute Lymphoblastic Leukemia (ALL) is a life-threatening hematological malignancy requiring rapid diagnosis.
  • Traditional diagnostic methods for ALL are labor-intensive and prone to inter-observer variability.
  • Current deep learning models for ALL detection can overfit and lack interpretability.

Purpose of the Study:

  • To develop a reliable and responsive computer-aided diagnostic (CAD) platform for ALL.
  • To enhance the accuracy and reduce variability in ALL diagnostics.
  • To improve patient outcomes through early and precise ALL detection.

Main Methods:

  • An ensemble-based model combining EfficientNetB3, EfficientNetV2B3, and EfficientNetV2B1 was developed.
Keywords:
Acute Lymphoblastic Leukemia (ALL)Fast Osprey Optimization (FOO)computer-aided diagnosis (CAD)deep learningensemble learningmedical image analysis

Related Experiment Videos

Last Updated: May 19, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • The model was optimized using Fast Osprey Optimization (FOO) for dynamic ensemble weight assignment.
  • Data augmentation was applied to an extensive dataset to address class imbalance and improve generalization.

    • Main Results:

    • The FOO-Ensemble model achieved high performance: 97.76% accuracy, 98.13% precision, 97.71% recall, and 97.83% F1-score.
    • The ensemble approach reduced inference time compared to individual models.
    • The framework demonstrated robustness, scalability, and superior generalization capabilities.

    Conclusions:

    • Deep learning ensembles with bio-inspired optimization offer trustworthy ALL detection.
    • Dynamic weighting mechanisms enhance stability and minimize overfitting risks.
    • The FOO-Ensemble framework shows potential for clinical application, assisting hematopathologists in accurate ALL diagnosis.