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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
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An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.

Oguzhan Katar1, Ozal Yildirim1,2

  • 1Department of Software Engineering, Firat University, Elazig 23119, Turkey.

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Summary
This summary is machine-generated.

This study introduces an explainable Vision Transformer (ViT) model for automated white blood cell (WBC) classification from blood films, achieving high accuracy and aiding pathologists in disease detection.

Keywords:
Score-CAMdeep learningexplainable AI modelsvision transformerswhite blood cells

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Area of Science:

  • Hematology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual white blood cell (WBC) subtype identification from blood films is laborious and prone to errors.
  • Existing deep learning models like CNNs struggle with long-range dependencies and global context in image analysis.
  • Accurate WBC classification is critical for diagnosing infections, leukemia, and other hematological malignancies.

Purpose of the Study:

  • To develop an explainable Vision Transformer (ViT) model for automated WBC detection and classification from blood film images.
  • To improve the accuracy and efficiency of WBC analysis compared to manual methods and existing automated approaches.
  • To provide a reliable and interpretable tool for pathologists to aid in disease diagnosis.

Main Methods:

  • Utilized a Vision Transformer (ViT) model incorporating a self-attention mechanism for feature extraction from WBC images.
  • Trained and validated the model on a public dataset of 16,633 WBC samples across five subtypes.
  • Conducted a secondary binary classification training for Granulocytes and Agranulocytes to address observed misclassifications.
  • Employed the Score-CAM algorithm for visualizing model predictions and ensuring reliability.

Main Results:

  • The ViT model achieved 99.40% accuracy in classifying five WBC subtypes.
  • A binary classification ViT model for Granulocytes and Agranulocytes reached 99.70% accuracy, 99.54% recall, 99.32% precision, and 99.43% F-1 score.
  • Analysis of misclassifications revealed a correlation with the presence or absence of cell granules.
  • Score-CAM visualization confirmed the model's focus on relevant image regions.

Conclusions:

  • The proposed explainable ViT model demonstrates superior performance and reliability for automated WBC classification.
  • Its ability to capture global context and provide interpretable results makes it suitable for clinical applications.
  • This AI-driven approach can significantly enhance the efficiency and accuracy of WBC analysis, facilitating faster and more precise diagnoses for pathologists.