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paperarXivTrust 82 · PrimaryPublished 4d agoLive · 2d ago

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

Automated malaria diagnosis from stained blood-smear microscopy is dominated by deep convolutional neural networks that are accurate but computationally expensive, poorly interpretable, and rarely validated with patient-level rigor. We present EMFE (Efficient Mathematical Feature Extraction), a five-feature framework for classifying single red-blood-cell images as parasitized or uninfected using Gray World color normalization, adaptive green-channel thresholding, morphological spot detection, and classical machine learning. Using the NIH LHNCBC malaria dataset (27,558 images from 200 patients)

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newsA deep learning optimized model for classification and detection of rice leaf diseases - Nature newsClassification of Benign Hematogones and B-cell Acute Lymphoblastic Leukemia in Peripheral Blood Smear Images Using Artificial Intelligence - Cureus newsA low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning - Nature newsDeep Learning Speeds Lymph Node Metastasis Detection - EMJ newsBreast cancer detection and classification via a robust deep learning approach - Nature

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repostefan-jansen/machine-learning-for-trading repoDataTalksClub/machine-learning-zoomcamp repoEthicalML/awesome-production-machine-learning repoaymericdamien/TopDeepLearning reporasbt/python-machine-learning-book

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personMd Abdullah Al Kafi personWalayat Hussain personMousumi Karmakar personSumit Kumar Banshal personAhmed Al Marouf

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