4 citations · 4 across the 4 of their papers we have counts for
5 papers
CoSWA-YOLOv12: Scale-Invariant Tiny Object Detection and Segmentation of Malaria Parasites
Ahmed Tahiru Issah, Carine Mukamakuza
Automated microscopy could widen access to malaria diagnosis in low-resource settings, but the deadliest species, P. falciparum, presents in its early ring stage as an object only…
On-Device Multi-Species Malaria Detection with Uncertainty-Calibrated Slide-Level Aggregation
Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt +1
Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce. Automated diagnosis based on microscopy images thus has strong pot…
SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears
Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without…
First African Digital Humanism Summer School 2025
Carine P. Mukamakuza, Monika Lanzenberger, George Metakides +2
Artificial intelligence (AI) has become a transformative force across global societies, reshaping the ways we communicate, collaborate, and make decisions. Yet, as AI systems incre…
Machine Learning-Based Analysis of ECG and PCG Signals for Rheumatic Heart Disease Detection: A Scoping Review (2015-2025)
Damilare Emmanuel Olatunji, Julius Dona Zannu, Carine Pierrette Mukamakuza +6
AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection i…