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Updated: May 29, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Two-stage CNN-based framework for leukocytes classification.

Siraj Khan¹, Muhammad Sajjad¹, José Escorcia-Gutierrez²

¹Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, 25120, Peshawar, Pakistan.

Computers in Biology and Medicine

|February 6, 2025

Summary

This summary is machine-generated.

This study introduces a two-stage CNN framework for accurate white blood cell (WBC) segmentation and classification. The method enhances diagnostic accuracy in blood analysis, improving patient outcomes.

Keywords:

Blood smear images Deep learning Image classification Image segmentation MobileNetV3 White blood cells YOLOv8

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence in Healthcare

Background:

Leukocytes (white blood cells) are critical diagnostic markers for various diseases.
Manual counting of leukocytes from peripheral blood smears is labor-intensive and prone to errors.
Accurate segmentation and classification of leukocytes in microscopic images are essential for reliable diagnostics.

Purpose of the Study:

To develop a robust automated system for precise leukocyte segmentation and classification.
To improve the accuracy and efficiency of blood microscopic image analysis.
To enhance diagnostic performance and patient outcomes through advanced AI techniques.

Main Methods:

A two-stage Convolutional Neural Network (CNN) framework was proposed.
YOLOv8m-seg was utilized for the precise segmentation of white blood cells (WBCs).
MobileNetV3 was employed for the classification of segmented WBCs into five types: lymphocytes, monocytes, basophils, eosinophils, and neutrophils.

Main Results:

The proposed framework achieved high accuracy in segmentation (up to 99.56%) and classification (up to 99.63%) across multiple datasets (Raabin-WBC, PBC, LISC).
The system demonstrated superior performance compared to existing state-of-the-art methods.
The automated approach significantly improved accuracy and efficiency in leukocyte analysis.

Conclusions:

The developed two-stage CNN framework offers a highly accurate and efficient solution for automated leukocyte segmentation and classification.
This AI-driven approach has the potential to significantly advance diagnostic capabilities in hematology.
The enhanced accuracy in leukocyte categorization promises improved patient diagnosis and treatment outcomes.