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arXiv Computer Vision By Md Abdullah Al Kafi, Walayat Hussain, Mousumi Karmakar, Sumit Kumar Banshal, Ahmed Al Marouf

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

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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.

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 Computer Vision.

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