Abstract
Nowadays, the classification of blood cell subtypes constitutes a
typical method for diagnosing many diseases, infections and
#inflammations. The application of an efficient cell classification
method is considered essential in modern diagnostic #medicine in order to
increase the number of analyzed cells per patient and decrease the
analysis time. The recent advances in digital technologies and the
#vigorous widespread of the Internet have ultimately led to the
development of large #repositories of images. Due to the effort and
expense involved in labeling data, training datasets are of a limited
size, while in contrast, electronic medical record systems contain a
significant number of unlabeled images. Semi-supervised learning
#algorithms constitute the appropriate machine learning methodology to
exploit the knowledge hidden in the unlabeled data with the explicit
classification information of labeled data for building powerful and
effective classifiers. In this work, we evaluate the performance of an
ensemble #semi-supervised learning algorithm for the classification of
blood cells subtypes. The efficacy of the presented algorithm is
illustrated by a series of experiments, demonstrating that reliable and
robust prediction models could be developed by the adaptation of
ensemble techniques in the #semisupervised learning framework.
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