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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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An efficient computer vision-based approach for acute lymphoblastic leukemia prediction.

Ahmad Almadhor¹, Usman Sattar², Abdullah Al Hejaili³

¹Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.

Frontiers in Computational Neuroscience

|December 12, 2022

Summary

This study introduces an automated leukemia prediction method using machine learning. Support Vector Machine achieved 90.0% accuracy, offering a faster alternative to manual diagnosis for acute lymphocytic leukemia (ALL).

Keywords:

acute lymphocytic leukemia feature extraction feature selection leukemia prediction machine learning voting algorithm

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Area of Science:

Medical Diagnostics
Computational Biology
Machine Learning

Background:

Leukemia, a blood cancer, involves imbalanced White Blood Cell (WBC) counts.
Acute Lymphocytic Leukemia (ALL) affects all ages, necessitating early detection for improved survival rates.
Manual leukemia prediction is costly and time-consuming, highlighting the need for automated methods.

Purpose of the Study:

To develop and evaluate an ensemble automated approach for leukemia prediction.
To compare the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms for leukemia detection.

Main Methods:

Utilized the C-NMC leukemia dataset from Kaggle, classifying images into cancer and healthy cells.
Employed data preprocessing including image cropping and MinMaxScaler normalization.
Extracted features using pre-trained Deep Neural Network (DNN) architectures (VGG19, ResNet50, ResNet101).
Applied feature selection techniques: Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF).
Implemented ensemble voting with classification algorithms on selected features.

Main Results:

The Support Vector Machine (SVM) algorithm achieved the highest accuracy of 90.0%.
The ensemble approach integrated multiple machine learning models for enhanced prediction.
Feature extraction and selection methods were crucial for optimizing model performance.

Conclusions:

The proposed automated ensemble prediction approach demonstrates significant potential for accurate and efficient leukemia detection.
SVM shows superior performance compared to other tested algorithms for this specific task.
Automated methods can expedite diagnosis, enabling earlier treatment initiation and potentially increasing patient survival rates.