What does this research mean for the field?

Machine learning models, specifically VGG16, DenseNet121, and InceptionV3, can accurately detect and classify anemia from blood cell images, achieving an accuracy of up to 93.43%. Novelty: ClaimNovelty.CONFIRMATORY. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to evaluate machine learning models for the detection and classification of anemia from blood cell images.

February 19, 2026International Journal on Smart Sensing and Intelligent SystemsOpen Access

Deep learning in public health: evaluating anemia detection methods

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The research aims to evaluate machine learning models for the detection and classification of anemia from blood cell images.
Utilized deep learning models: VGG16, InceptionV3, and DenseNet121.
Trained on a dataset of approximately 1000 blood cell images.
Assessed model performance using metrics including F1-score, recall, accuracy, and precision.
Achieved an accuracy of 93.43% with the VGG16 model.
DenseNet121 reached an accuracy of 90.48%.
InceptionV3 recorded an accuracy of 78.80%.

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Implication

Evaluating deep learning techniques for accurate anemia classification from blood cell images, suggesting effective screening tools.

Cite This Study

Ahuja et al. (2026) studied this question.

synapsesocial.com/papers/6996a8d4ecb39a600b3efef1 https://doi.org/10.2478/ijssis-2026-0007

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Deep learning in public health: evaluating anemia detection methods

Key Points

Discussion

Implication

Cite This Study

Deep learning in public health: evaluating anemia detection methods

Key Points

Discussion

Implication

Cite This Study

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