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Classification of Leukocytes

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White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep

Shahid Rashid¹, Mudassar Raza², Muhammad Sharif¹

¹Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, 47040, Pakistan.

Scientific Reports

|October 19, 2023

Summary

This study introduces a novel method for classifying white blood cells (WBCs) using a virtual hexagonal trellis (VHT) structure and deep learning. The approach achieves 99.9% accuracy, significantly improving disease diagnosis capabilities.

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Area of Science:

Medical Imaging
Computational Biology
Machine Learning

Background:

Accurate white blood cell (WBC) classification is crucial for diagnosing various medical conditions.
Existing methods for WBC categorization face challenges in efficiency and precision.
Automated analysis of blood cell images is essential for clinical diagnostics.

Purpose of the Study:

To develop an advanced feature extraction and classification model for white blood cells.
To enhance the accuracy and efficiency of WBC categorization for improved disease diagnosis.
To introduce a novel virtual hexagonal trellis (VHT) structure for feature extraction.

Main Methods:

Utilized a combination of Graft Net Convolutional Neural Network (CNN) and a proposed virtual hexagonal trellis (VHT) for feature extraction.
Employed ant colony optimization (ACO) to refine CNN-extracted features for optimal acquisition.
Integrated VHT and ACO features into a single vector for classification using support vector machine (SVM) variants.

Main Results:

The proposed method achieved a classification accuracy of 99.9% for white blood cell categorization.
The integrated feature extraction approach significantly outperformed existing methods.
The VHT structure proved effective as a kernel-based filter for extracting relevant image features.

Conclusions:

The developed WBC classification strategy demonstrates superior performance and high accuracy.
The combination of VHT feature extraction, ACO optimization, and SVM classification offers a robust solution for medical image analysis.
This research contributes a significant advancement in automated disease diagnosis through precise WBC identification.