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Updated: Jun 3, 2025

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Published on: February 21, 2021

An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.

Pradeep Kumar Das¹, Sukadev Meher², Adyasha Rath³

¹Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal 506004, Telangana, India; School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu 632014, India.

ISA Transactions

|January 11, 2025

Summary

This summary is machine-generated.

A novel deep learning system accurately detects Acute Lymphoblastic Leukemia (ALL) using MobileNetV2 and ShuffleNet. This efficient model achieves high precision and sensitivity, crucial for early disease diagnosis and treatment.

Keywords:

Acute Lymphoblastic Leukemia Blood cancer Classification Deep learning Detection

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Early and accurate detection of Acute Lymphoblastic Leukemia (ALL) is critical for effective treatment and patient survival.
Deep learning, especially transfer learning, shows promise in medical image analysis, even with limited data.

Purpose of the Study:

To develop a novel, efficient, and accurate deep learning-based system for leukemia detection.
To improve the discrimination ability and receptive field for enhanced classification performance.

Main Methods:

A hybrid deep learning model combining MobileNetV2 and ShuffleNet for leukemia detection.
Integration of inverted residual bottleneck, depthwise separable convolution, and channel shuffling for improved feature discrimination.
Experimental selection of an optimal threshold value and weight factor for balancing efficiency and performance.

Main Results:

The proposed framework achieved superior detection performance on the ALLIDB1 dataset with 99.07% accuracy, 100% sensitivity, and 98.00% precision.
On the ALLIDB2 dataset, the system demonstrated 98.46% accuracy, 98.46% sensitivity, and 98.46% precision.
The model outperformed existing methods in key performance metrics, including accuracy, precision, sensitivity, specificity, and F1 score.

Conclusions:

The developed deep learning system offers a faster and more accurate approach to leukemia detection.
The integration of MobileNetV2 and ShuffleNet, along with optimized parameters, significantly enhances diagnostic capabilities.
This novel framework holds potential for improving early diagnosis and treatment outcomes for patients with Acute Lymphoblastic Leukemia.