What question did this study set out to answer?

To develop a novel framework that accurately classifies Acute Myeloid Leukemia (AML) subtypes using cell images.

December 9, 2025Discover OncologyOpen Access

Acute Myeloid Leukemia subtype classification using single blood cell images

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Authors

RCRhea ChainaniSymbiosis International University RVRamitha VimalaSymbiosis International University SSSakshi SahSymbiosis International University

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Implication

This framework applies machine learning for accurate identification of Acute Myeloid Leukemia subtypes from cell images, suggesting enhanced diagnostic methods.

Key Points

To develop a novel framework that accurately classifies Acute Myeloid Leukemia (AML) subtypes using cell images.
Utilized a two-step classification strategy for AML detection and subtype classification.
Implemented custom-trained CNNs and conventional machine learning algorithms in an ensemble model.
Addressed domain shift with a robust preprocessing pipeline including stain normalization and brightness correction.
Achieved 94.7% accuracy, 95.2% precision, and 94.8% F1-score in single cell classification.
Proposed ensemble model achieved 94% accuracy in distinguishing healthy from AML patients.

Cite This Study

Chainani et al. (2025) studied this question.

synapsesocial.com/papers/69401d682d562116f28f9228 https://doi.org/10.1007/s12672-025-04206-3

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Implication

This framework applies machine learning for accurate identification of Acute Myeloid Leukemia subtypes from cell images, suggesting enhanced diagnostic methods.

Acute Myeloid Leukemia subtype classification using single blood cell images

Discussion

Member takes

Implication

Key Points

Cite This Study

Acute Myeloid Leukemia subtype classification using single blood cell images

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider