MACHINE LEARNING TECHNIQUES TO DIAGNOSE BREAST CANCER FROM IMAGE-PROCESSED NUCLEAR FEATURES OF FINE NEEDLE ASPIRATES

MACHINE LEARNING TECHNIQUES TO DIAGNOSE BREAST CANCER FROM IMAGE-PROCESSED NUCLEAR FEATURES OF FINE NEEDLE ASPIRATES

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by William H. Wolberg , W. Nick Street , O. L. Mangasarian

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@MISC{Wolberg_machinelearning,
    author = {William H. Wolberg and W. Nick Street and O. L. Mangasarian},
    title = { MACHINE LEARNING TECHNIQUES TO DIAGNOSE BREAST CANCER FROM IMAGE-PROCESSED NUCLEAR FEATURES OF FINE NEEDLE ASPIRATES},
    year = {}
}

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Abstract

An interactive computer system evaluates and diagnoses based on cytologic features derived directly from a digital scan of fine-needle aspirates (FNA) slides. A consecutive series of 569 patients provided the data to develop the system and an additional 54 consecutive, new patients provided samples to test the system. The projected prospective accuracy of the system estimated by ten-fold cross validation was 97%. The actual accuracy on 54 new samples (36 benign, 1 atypia, and 17 malignant) was 100%. Digital image

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