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Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.

Şafak Kılıç1

  • 1Department of Software Engineering, Kayseri University, Kayseri, Turkey. safakkilic@kayseri.edu.tr.

Journal of Imaging Informatics in Medicine
|April 29, 2025
PubMed
Summary

A new dual-path deep learning model accurately classifies blood cells using ConvNeXt and Swin Transformers. This advanced medical image analysis tool achieves 99.98% accuracy, improving hematological disorder diagnosis.

Keywords:
Blood cell classificationConvNeXtFeature fusionMedical image analysisSwin Transformer

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Area of Science:

  • Medical Image Analysis
  • Hematology
  • Deep Learning

Background:

  • Accurate blood cell classification is vital for diagnosing hematological disorders.
  • Traditional methods are labor-intensive and can be inconsistent.
  • Automated analysis is needed to improve diagnostic efficiency.

Purpose of the Study:

  • To develop a highly accurate automated blood cell classification system.
  • To leverage deep learning for enhanced feature extraction and context integration.
  • To improve the quality of medical images for better cellular differentiation.

Main Methods:

  • A dual-path deep learning architecture combining ConvNeXt and Swin Transformer networks.
  • Implementation of a Multi-scale Preprocessing Module (MPM) for image enhancement.
  • Training and validation on a dataset of 17,092 blood cell images.

Main Results:

  • Achieved an unprecedented classification accuracy of 99.98%.
  • Demonstrated superior performance compared to existing blood cell analysis methods.
  • The MPM significantly improved the visibility and differentiation of cellular features.

Conclusions:

  • The proposed dual-path deep learning model offers a highly accurate solution for blood cell classification.
  • This technology has the potential to enhance clinical diagnostic workflows and patient outcomes.
  • Automated, precise blood cell analysis can reduce medical professional workload and improve diagnostic speed.