What question did this study set out to answer?

The study aims to compare the effectiveness of various AI architectures for sickle cell disease classification.

February 2, 2026DiagnosticsOpen Access

Toward AI-Assisted Sickle Cell Screening: A Controlled Comparison of CNN, Transformer, and Hybrid Architectures Using Public Blood-Smear Images

View Full Paper

Ask AI

Bookmark

Authors

Controlled comparison evaluates AI architectures for SCD classification, highlighting CNN advantages.

The study aims to compare the effectiveness of various AI architectures for sickle cell disease classification.
Controlled benchmark of CNN, Vision Transformers, and hybrid architectures.
Utilized a public peripheral blood smear dataset for image-level classification.
Models were fine-tuned under identical conditions with robust evaluation protocols.
Performance assessed using accuracy, precision, recall, F1-score, and error analysis.
CNN and hybrid models showed higher and more stable performance compared to transformer-only models.
MaxViT-Tiny and DenseNet121 performed the best overall.
Pure Vision Transformers were less effective in data-constrained environments.
Error analysis highlighted false-positive predictions due to morphological challenges.

Tasji et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark