MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

Abstract:Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck. Existing deep learning systems face three compounding failures: end-to-end detectors treat unannotated cells as background, skewing recall by annotation completeness rather than true cell recovery; Non-Maximum Suppression suppresses valid detections in dense smears; and pipelines lack per-cell spatial evidence for clinical audit. We present MalariAI, a two-stage decoupled framework addressing all three. Stage 1 applies an annotation-agnostic watershed algorithm to isolate every cell in a full 1600x1200 image, recovering 75.95% of ground-truth cells without any ground-truth input. End-to-end, the pipeline reaches a binary parasitized AP@0.5 of 29.10% - the clinically relevant metric for flagging any infected cell - while the stricter multi-class mAP@0.5 of 8.67% mainly reflects watershed's organic region boundaries being penalized against axis-aligned ground-truth boxes, not a localisation failure. Stage 2 fine-tunes EfficientNet-B0 with Focal Loss on ground-truth crops, achieving 98.36% classification accuracy - an oracle upper bound once a cell is correctly localised - with 87.5% and 75.0% accuracy on the rare schizont and gametocyte stages, versus 38.45% and 57.27% AP for a modern YOLOv8s detector evaluated end-to-end on the same classes. Grad-CAM++ heatmaps generated per detected cell provide instance-level spatial evidence for clinical audit; a quantitative energy-in-box analysis confirms this activation is concentrated on the annotated cell body significantly above a geometric chance baseline (+0.0485, paired p = 1.4 x 10^-33), letting microscopists verify predictions at the individual parasite level without sacrificing classification performance.

Submitted to Array - Elsivier(under review). 4 authors, includes figures and appendix

Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2607.00385 [eess.IV]

(or arXiv:2607.00385v2 [eess.IV] for this version)

https://doi.org/10.48550/arXiv.2607.00385 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.