Investigation for Improvement of Automatic Classification Accuracy of Leukocyte Image using Machine Learning

DOI

Bibliographic Information

Other Title
  • 機械学習を用いた白血球画像の自動分類精度向上のための検討

Description

<p>Classifying leukocyte and examining their proportions is a very important for disease examination and diagnosis. This examination needs the knowledge of experts and a lot of time. Therefore, many automatic leukocyte images classification algorithms have been proposed. There is a method to classify 13 types blood cells using 1 vs 1 Support Vector Machine in one of them. In the conventional method, leukocyte images are classified with the 26-dimensional feature vectors. The feature vectors are composed of 18-dimensional feature vectors relating to the color of cell and 8-dimensional feature vectors relating to the form of cell. The classification accuracy, however, is poor with these feature vectors regarding granulocytes in this method. In this paper, we propose new feature vectors to improve the classification accuracy of promyelocytes, myelocytes, myelocytes, basophils, eosinophils with low classification accuracy among the leukocyte fractions. That is, we add two feature vectors in the proposed method.</p>

Journal

Details 詳細情報について

  • CRID
    1390001205269947648
  • NII Article ID
    130006077079
  • DOI
    10.11239/jsmbe.55annual.392
  • ISSN
    18814379
    1347443X
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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