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
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
arXiv:2607. 00385v2 Announce Type: replace-cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck.
By Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman
arXiv:2608. 08566v1 Announce Type: cross Abstract: Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce.
By Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
By Sagnik Ghosh
The paper introduces two YOLOv11-based instance segmentation models that simultaneously perform wound boundary segmentation and wound classification across five clinically relevant wound types. Using a balanced dataset of 2,963 annotated images and data augmentation, the models achieve high performance, with YOLOv11x excelling in boundary segmentation and YOLOv11m and YOLOv11l leading in classification metrics. The lightweight YOLOv11n variant offers comparable accuracy with lower computational demands, making it suitable for resource-constrained clinical and remote care deployments.
By Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Igor Melnychuk, Ming C. Leu