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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Multimodal Imaging of Stem Cell Implantation in the Central Nervous System of Mice
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Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification

Khaled Tarmissi1, Jamal Alsamri2, Mashael Maashi3

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Mecca, Saudi Arabia.

Scientific Reports
|April 24, 2025
PubMed
Summary

This study introduces a new AI method for classifying bone marrow cells from images, achieving 98.60% accuracy. This advancement aids in faster and more precise hematology diagnostics.

Keywords:
Bone marrow cell classificationHistopathological imagesImage preprocessingMultimodal feature extractionSnake optimization algorithm

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Area of Science:

  • Biomedical imaging
  • Hematology
  • Artificial intelligence in medicine

Background:

  • Bone marrow (BM) cell identification is vital for hematology diagnostics but current physical methods are time-consuming and challenging.
  • Deep learning (DL) and machine learning (ML) offer advanced solutions for cell detection and classification using imaging data.

Purpose of the Study:

  • To propose a novel Multimodal Transfer Learning with Snake Optimization on Bone Marrow Cell Classification (MTLSO-BMCC) technique.
  • To accurately identify and classify bone marrow cells using histopathological images (HI).

Main Methods:

  • Image preprocessing using a median filter (MF) for noise reduction.
  • Multimodal feature extraction using InceptionV3, Deep SqueezeNet, and SE-DenseNet models.
  • Bone marrow cell classification via a hybrid kernel extreme learning machine (HKELM) optimized by the snake optimization algorithm (SOA).

Main Results:

  • The MTLSO-BMCC technique achieved a high classification accuracy of 98.60%.
  • Demonstrated superior performance compared to existing bone marrow cell classification approaches.

Conclusions:

  • The proposed MTLSO-BMCC technique offers an effective and accurate solution for bone marrow cell classification.
  • This AI-driven approach can significantly improve the efficiency and precision of hematology diagnostics.