← Back to all news
arXiv AI By Samuel A. Adeniji, Goodness C. Obasi, Chris-Victor Ntwali, Aondana M. Iorumbur, Confidence Raymond, Lowami Uwimana, Ahmed Tahiru Issah

Investigating White Blood Cells as a Source of False-Positive Malaria Parasite Detection in African Blood-Smear Images

Read the original on arXiv AI →

The study examined whether white blood cells (WBCs) cause false‑positive malaria detections in Giemsa‑stained blood smears. Two YOLOv12 models—one trained only on parasite labels and another on both parasite and WBC labels—were evaluated on 8,000 images from Uganda and Ghana. Across seven spatial and statistical tests, false positives did not cluster near WBCs; instead, most were background detections, and the multi‑task model (with WBC labels) performed better overall, suggesting that WBC labeling alone does not reduce false positives but that stain artifacts and unannotated ring forms are the true sources of error.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Jul 2

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

arXiv:2607. 00385v1 Announce Type: cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of expert microscopists remains the primary bottleneck to timely and accurate diagnosis.

By Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman
computer-vision
More like this →
arXiv Machine Learning
Jul 21

SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears

arXiv:2607. 16324v1 Announce Type: cross Abstract: Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without providing morphological evidence for their predictions, limiting the ability of microscopists to audit those predictions at the case level.

By Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
llmsragmultimodal
More like this →
arXiv Computer Vision
Aug 26

EMFE: A lightweight, explainable machine learning framework for malaria cell classification

EMFE (Efficient Mathematical Feature Extraction) is a lightweight, explainable machine‑learning framework that classifies single red‑blood‑cell images as parasitized or uninfected using five engineered features: Gray World color normalization, adaptive green‑channel thresholding, morphological spot detection, and classical classifiers. On the NIH LHNCBC malaria dataset (27,558 images from 200 patients), a tuned Random Forest achieved 94.6% pooled out‑of‑fold accuracy, 94.3% on a 40‑patient holdout, and outperformed deep‑learning baselines in an accuracy‑efficiency trade‑off. Ablation studies, synthetic perturbations, and explainability analyses identified spot saturation as the dominant discriminative feature and quantified the framework’s failure modes and patient‑level performance.

By Md Abdullah Al Kafi, Walayat Hussain, Mousumi Karmakar, Sumit Kumar Banshal, Ahmed Al Marouf
benchmarks
More like this →
arXiv AI
Sep 25

CoSWA-YOLOv12: Scale-Invariant Tiny Object Detection and Segmentation of Malaria Parasites

CoSWA-YOLOv12 is a compact YOLOv12 instance‑segmentation detector designed to improve detection of tiny malaria parasites in microscopy images. It introduces a Cooperative Scale‑adaptive Wasserstein Assignment that applies a Wasserstein distance‑based label assignment inversely proportional to object size, a wavelet detail residual, and a min‑max Gaussian regression loss, all of which preserve pretrained weights. On a five‑class Rwandan thick‑smear dataset, the method raises P. falciparum recall from 0.63 to 0.74, increases mAP@50 from 0.73 to 0.81, and reduces missed detections from 38% to 15%.

By Ahmed Tahiru Issah, Carine Mukamakuza
computer-visionbenchmarks
More like this →