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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Automatic base-model selection for white blood cell image classification using meta-learning.

Eduardo Rivas-Posada¹, Mario I Chacon-Murguia¹

¹Tecnologico Nacional de Mexico / I T Chihuahua, Visual Perception Lab, Ave. Tecnologico #2909, Chihuahua, 31310, Mexico.

Computers in Biology and Medicine

|July 2, 2023

Summary

This summary is machine-generated.

This study introduces a new method for automatically selecting deep-learning models for White Blood Cell (WBC) image classification. The approach enhances diagnostic accuracy, even with challenging medical datasets.

Keywords:

Automatic model selection Imbalanced dataset Meta-learning Meta-transfer learning White blood cells classification

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Area of Science:

Medical Image Analysis
Machine Learning in Healthcare
Computational Pathology

Background:

Deep learning models are crucial for medical image classification, including White Blood Cell (WBC) analysis for diagnosing pathologies like leukemia.
Medical datasets often present challenges such as imbalance, inconsistency, and high collection costs, hindering optimal model selection.
Existing methods struggle to effectively choose appropriate deep learning models for diverse WBC image datasets acquired with varying methods.

Purpose of the Study:

To propose a novel methodology for the automatic selection of deep learning models for White Blood Cell (WBC) classification tasks.
To address the challenges posed by imbalanced, inconsistent, and diverse medical imaging data.
To improve the performance and reliability of WBC classification in clinical settings.

Main Methods:

A two-level learning approach combining meta- and base-level learnings.
Meta-learning utilizing meta-models and prior-models to acquire meta-knowledge via meta-tasks with a shades of gray color constancy method.
An algorithm employing meta-knowledge and Centered Kernel Alignment for model selection, followed by learning rate finder adaptation and ensemble learning.

Main Results:

Achieved high accuracy (98.29%) and balanced accuracy (97.69%) on the Raabin dataset.
Reached perfect accuracy (100%) on the BCCD dataset.
Obtained excellent accuracy (99.57%) and balanced accuracy (99.51%) on the UACH dataset, outperforming state-of-the-art models.

Conclusions:

The proposed methodology effectively automates the selection of optimal deep learning models for WBC classification tasks.
The approach demonstrates superior performance compared to existing state-of-the-art models across multiple datasets.
The methodology shows potential for extension to other medical image classification tasks facing data limitations and variability.