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

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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.
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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.