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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...

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Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep

Md Manowarul Islam1, Habibur Rahman Rifat1, Md Shamim Bin Shahid1

  • 1Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered framework using deep learning to automatically detect leukemia from blood images. The novel fusion model achieves high accuracy, offering a faster and more efficient diagnostic tool for acute lymphoblastic leukemia (ALL).

Keywords:
DenseNet-121VGG16feature fusioninternet of medical thingsleukemiasegmentationtransfer learning

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Acute lymphoblastic leukemia (ALL) diagnosis is challenging, requiring time-consuming and expensive specialist tests.
  • Early ALL detection is crucial for timely and effective treatment initiation.
  • Advancements in Artificial Intelligence (AI) and Internet of Things (IoT) offer new diagnostic possibilities.

Purpose of the Study:

  • To introduce a novel AI-based Internet of Medical Things (IoMT) framework for automated leukemia detection from peripheral blood smear (PBS) images.
  • To develop and evaluate a deep learning-based fusion model for accurate ALL classification.
  • To enhance the speed and efficiency of leukemia diagnosis.

Main Methods:

  • A fusion deep learning model was developed, utilizing two input channels: original and segmented PBS images.
  • VGG16 and DenseNet-121 were employed for feature extraction from original and segmented images, respectively.
  • The model was trained on 6512 images from 89 individuals and evaluated for classification performance.

Main Results:

  • The proposed fusion model achieved high diagnostic accuracy (99.89%), precision (99.80%), and recall (99.72%).
  • The model demonstrated superior performance compared to several state-of-the-art Convolutional Neural Network (CNN) models.
  • A web application (Beta Version) was developed for simulating the leukemia detection methodology.

Conclusions:

  • The developed AI-based IoMT framework and fusion model show significant potential for accurate and efficient leukemia detection.
  • This approach can potentially save lives and reduce diagnostic efforts.
  • The findings have implications for advancing computer-aided leukemia detection in biomedical research.