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 Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

2.8K
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...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Unified biopolymer-based film design exploiting the multifunctional roles of tannic acid for beef preservation and real-time freshness monitoring.

Food research international (Ottawa, Ont.)·2026
Same author

Integrated physiological, transcriptomic and metabolomic analysis reveal the tolerance thresholds and mechanisms of <i>Cynanchum auriculatum</i> to saline and alkaline stress.

Physiology and molecular biology of plants : an international journal of functional plant biology·2026
Same author

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BMC medical informatics and decision making·2026
Same author

A konjac glucomannan composite film embedding proanthocyanidin-grafted chitosan and carvacrol@ZIF-8 nanoparticles for synergistic antioxidant/antibacterial packaging.

Food chemistry·2026
Same author

Advancing ST-elevated myocardial infarction mortality risk prediction in Asian populations through explainable and calibrated machine learning.

Digital health·2026
Same author

Association of PD-L1 expression and clinical outcomes in ROS1-rearranged advanced non-small cell lung cancer treated with entrectinib.

Translational lung cancer research·2026

Related Experiment Video

Updated: Sep 17, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K

Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature

Sazzli Kasim1,2,3,4, Sorayya Malek5, JunJie Tang6

  • 1Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.

Scientific Reports
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid deep learning method for classifying leukemia subtypes from blood cell images. The approach combines Convolutional Neural Networks (CNNs) with traditional classifiers, achieving high accuracy in identifying different leukemia types.

Keywords:
Acute lymphoblastic leukemia image databaseCNNDeep learningLeukemiaMunich AML morphology dataset

More Related Videos

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

6.9K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.1K

Related Experiment Videos

Last Updated: Sep 17, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K
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

6.9K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.1K

Area of Science:

  • Computational biology and bioinformatics
  • Medical imaging and diagnostics
  • Artificial intelligence in healthcare

Background:

  • Leukemia, a prevalent blood cancer, requires early and accurate diagnosis for effective treatment.
  • Peripheral blood smear analysis, a key diagnostic tool, faces challenges in subjective interpretation and expertise limitations.
  • Multiclass classification of leukemia subtypes using deep learning is hindered by limited data and morphological similarities.

Purpose of the Study:

  • To develop a novel hybrid methodology for robust multiclass classification of leukemia subtypes.
  • To address data scarcity and morphological similarities in leukemia subtype identification.
  • To enhance the speed and reliability of leukemia diagnosis for improved patient care.

Main Methods:

  • A hybrid approach combining pre-trained Convolutional Neural Networks (CNNs) (VGG16, InceptionV3, ResNet50) for feature extraction.
  • Integration with advanced classifiers: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP).
  • Utilized publicly available datasets: Acute Lymphoblastic Leukemia Image Database (ALL-IDB) and Munich AML Morphology Dataset.

Main Results:

  • The InceptionV3 + SVM combination achieved the highest accuracy of 88%.
  • VGG16 + XGBoost demonstrated strong performance with 87% accuracy.
  • MLP-based models showed effectiveness in capturing non-linear data patterns; ResNet50 faced overfitting issues.

Conclusions:

  • The hybrid methodology offers a scalable and precise tool for leukemia subtype identification, particularly in data-constrained environments.
  • This approach significantly improves the speed and reliability of diagnosis compared to traditional methods.
  • The study highlights the potential of integrating deep learning with hybrid classification for enhanced clinical decision-making in leukemia diagnostics.