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Classification of Leukocytes

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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Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell

Jose Luis Diaz Resendiz¹, Volodymyr Ponomaryov¹, Rogelio Reyes Reyes¹

¹Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico.

|July 14, 2023

Summary

This study introduces an Explainable AI (XAI) system for leukemia diagnosis, enhancing computer-aided diagnosis (CAD) reliability. The method uses White Blood Cell (WBC) segmentation to improve deep learning accuracy and provide visual explanations for diagnoses.

Keywords:

XAI acute lymphoblastic leukemia deep-learning leukemia classification nuclei segmentation

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Published on: March 26, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Leukemia poses significant health challenges with high mortality rates.
Computer-aided diagnosis (CAD) shows promise but faces reliability issues due to the
black box problem
in deep learning models.

Purpose of the Study:

To develop an Explainable AI (XAI) leukemia classification method.
To address the unreliability of deep learning in medical diagnosis.
To enhance CAD systems for leukemia with improved interpretability and accuracy.

Main Methods:

Integrating White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism.
Utilizing a combination of image processing and U-Net techniques for WBC segmentation.
Employing modified ResNet-50 models with tested MLP classifiers, activation functions, and training schemes for subtype classification.
Incorporating visual explainability and feature space analysis for interpretable classification.

Main Results:

Achieved an Intersection over Union (IoU) of 0.91 for the segmentation algorithm across six databases.
Attained a 99.9% accuracy for the deep learning classifier in leukemia subtype testing.
Confirmed improved network focus on segmented images via Grad CAM and clustering space analysis compared to non-segmented images.

Conclusions:

The proposed Explainable AI (XAI) system enhances leukemia classification accuracy and reliability.
Visual explainability and feature analysis provide interpretable insights into the diagnostic process.
This CAD system has the potential to aid physicians in leukemia diagnosis, ultimately improving patient outcomes.