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Erschienen in:
2019 | OriginalPaper | Buchkapitel
25. A Novel Deep Learning Approach in Haematology for Classification of Leucocytes
verfasst von : Vitoantonio Bevilacqua, Antonio Brunetti, Gianpaolo Francesco Trotta, Domenico De Marco, Marco Giuseppe Quercia, Domenico Buongiorno, Alessia D’Introno, Francesco Girardi, Attilio Guarini
Erschienen in: Quantifying and Processing Biomedical and Behavioral Signals
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Abstract
This paper presents a comparison between two different Computer Aided Diagnosis systems for classification of five types of leucocytes located in the tail of a Peripheral Blood Smears: Lymphocytes, Monocytes, Neutrophils, Basophils and Eosinophils. In particular, we have evaluated and compared the performance of a previous feature-based Back Propagation Neural Network classifier with the performance of two novel classifiers both based on Deep Learning using Convolutional Neural Networks introduced in this study. All the classifiers are built considering the same dataset of images acquired in a previous study. The experimental results, reported in terms of accuracy, sensitivity, specificity and precision, show that the different strategies could be compared and discussed from both clinical and technical point of view.