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Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification.

Priyanka Rana¹, Arcot Sowmya¹, Erik Meijering¹

¹School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

Scientific Reports

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for imbalanced blood cell classification using advanced data augmentation techniques. The approach effectively addresses data imbalance, improving classification accuracy for minority cell types.

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Area of Science:

Biomedical image analysis
Machine learning in healthcare
Hematology

Background:

Accurate classification of human blood cells is crucial for clinical decisions.
Automated systems offer efficiency and objectivity over manual methods.
Deep learning, particularly Convolutional Neural Networks (CNNs), excels in image classification but struggles with imbalanced datasets.

Purpose of the Study:

To develop an effective method for classifying imbalanced blood cell datasets.
To improve the performance of classifiers on minority blood cell classes.
To address the bias in automated classification caused by data imbalance.

Main Methods:

Utilized Wasserstein divergence Generative Adversarial Networks (GANs) for data augmentation.
Implemented mixup and a novel nonlinear mixup technique for oversampling minority classes.
Developed a minority class-focused sampling strategy to ensure representation of augmented data.

Main Results:

The proposed method demonstrated superior performance in classifying imbalanced blood cell data.
Evaluated on human T-lymphocyte and Red Blood Cell datasets, the approach showed significant improvements.
Achieved higher classification performance, as measured by the F1-score, compared to existing methods.

Conclusions:

The developed data augmentation and sampling strategy effectively mitigates data imbalance in blood cell classification.
This approach enhances the reliability and accuracy of automated blood cell analysis.
The findings suggest a promising direction for improving machine learning models in clinical hematology.