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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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A Novel Deep Learning Based Approach With Hyperparameter Selection Using Grey Wolf Optimization For Leukemia Classification And Hematologic Malignancy Detection.

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A Novel Deep Learning Based Approach With Hyperparameter Selection Using Grey Wolf Optimization For Leukemia Classification And Hematologic Malignancy Detection.

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A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia

Shams Ur Rehman¹, Robertas Damaševicius², Hassan Al Sukhni³

¹Department of Computer Science, NUTECH University, Islamabad, Pakistan.

Peerj. Computer Science

|September 24, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

Customized CNN Grey wolf optimization Leukemia cancer Self-attention Vision transformer

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This study introduces an automated deep learning framework for leukemia classification, improving diagnostic accuracy. The novel approach enhances microscopic images and utilizes advanced neural networks for precise cancer detection.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Traditional leukemia diagnosis relies on manual microscopic analysis of blood smears, which is subjective and error-prone.
Automated methods are needed to improve the accuracy and efficiency of leukemia diagnosis.

Purpose of the Study:

To develop and evaluate a deep learning framework for automated leukemia classification from microscopic images.
To enhance image quality and leverage advanced AI models for improved diagnostic performance.

Main Methods:

A novel lightweight algorithm using hyperbolic sine function for contrast enhancement.
A customized Convolutional Neural Network (CNN) model incorporating a parallel inverted dual self-attention network (PIDSAN4) and a tiny Vision Transformer (ViT).

Hyperparameter tuning using grey wolf optimization for model training.

Main Results:

The proposed model achieved high performance metrics: 0.913 accuracy, 0.892 sensitivity, 0.925 specificity, 0.883 precision, 0.894 F-measure, and 0.901 G-mean.
Comparison with state-of-the-art pre-trained models demonstrated superior accuracy of the proposed framework.
The automated system offers a more objective and potentially faster diagnostic approach.

Conclusions:

The developed deep learning framework shows significant potential for accurate and automated leukemia diagnosis.
This approach can overcome the limitations of traditional manual methods, leading to better patient outcomes.
Further research can explore integration into clinical workflows for real-world application.