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PRISM method improves leukemia classification via perinuclear image segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called PRISM for segmenting images of blood cells to classify Acute Lymphoblastic Leukemia (ALL). This approach focuses on adaptive concentric zones around the nucleus rather than precise cell boundaries, which are often difficult to discern in low-contrast images. PRISM extracts cytoplasmic descriptors using color and texture information, feeding them into a stacked ensemble of traditional classifiers. The method achieved a high accuracy of 98.46% and a precision-recall AUC of 0.9937 on its classification task. AI

IMPACT Introduces a novel image segmentation technique that could improve diagnostic accuracy for blood-related diseases.

RANK_REASON Publication of an academic paper detailing a new method for image segmentation and classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PRISM method improves leukemia classification via perinuclear image segmentation

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Publication of an academic paper detailing a new method for image segmentation and classification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · André Ricardo Backes ·

    PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

    Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy ne…

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