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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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ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images.

Adel Sulaiman¹, Swapandeep Kaur², Sheifali Gupta²

¹Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|June 28, 2023

Summary

This study introduces ResRandSVM, a deep learning method for detecting Acute Lymphocytic Leukemia (ALL) in blood smears. The approach achieves high accuracy in identifying leukemia, offering a faster and more precise diagnostic tool.

Keywords:

DenseNet121 EfficientNetB0 InceptionV3 MobileNetV2 ResNet152 ResNet50 VGG16 acute lymphocytic leukemia adaboost analysis of variance artificial neural network deep learning machine learning naïve Bayes principal component analysis random forest support vector machine

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Area of Science:

Hematology
Oncology
Medical Imaging

Background:

Acute Lymphocytic Leukemia (ALL) is a rapidly progressing cancer impacting white blood cells.
Manual detection of ALL is time-consuming and prone to errors.
Machine learning and deep learning offer potential for faster, more accurate ALL diagnosis.

Purpose of the Study:

To develop and evaluate a deep feature selection-based approach for detecting Acute Lymphocytic Leukemia (ALL) in blood smear images.
To compare the performance of various deep learning models for feature extraction and classification.

Main Methods:

Employed seven deep learning models (ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0, ResNet50) for deep feature extraction from blood smear images.
Utilized three feature selection methods: Analysis of Variance (ANOVA), Principal Component Analysis (PCA), and Random Forest.
Classified images using Adaboost, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naïve Bayes classifiers.

Main Results:

The ResRandSVM model, combining ResNet50 for feature extraction, Random Forest for selection, and SVM for classification, demonstrated superior performance.
Achieved an accuracy of 0.900, precision of 0.902, recall of 0.957, and F1-score of 0.929.
Indicated the effectiveness of deep learning and feature selection in improving ALL detection.

Conclusions:

The proposed ResRandSVM approach provides an efficient and accurate method for the detection of Acute Lymphocytic Leukemia.
Deep learning techniques integrated with robust feature selection can significantly enhance diagnostic capabilities in hematological malignancies.
This automated approach holds promise for improving the speed and reliability of ALL diagnosis.