Journal ArticleDOI
Automatic segmentation, counting, size determination and classification of white blood cells
TLDR
A new and completely automatic counting, segmentation and classification process is developed that automatically counts the white blood cells, determine their sizes accurately and classifies them into five types such as basophil, lymphocyte, neutrophil, monocyte and eosinophil.About:
This article is published in Measurement.The article was published on 2014-09-01. It has received 135 citations till now.read more
Citations
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Journal ArticleDOI
White blood cells identification system based on convolutional deep neural learning networks.
TL;DR: A novel WBCs identification system based on deep learning theory is proposed and a high performance W BCsNet can be employed as a pre-trained network.
Journal ArticleDOI
Automatic detection and classification of leukocytes using convolutional neural networks
TL;DR: An algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation is proposed and has better effect almost than iterative threshold method with less cost time, and some classification experiments show that the proposed classification method has better accuracy almost than some other methods.
Journal ArticleDOI
White blood cells detection and classification based on regional convolutional neural networks.
TL;DR: A computer-aided automated system that can easily identify and locate WBC types in blood images has been proposed and showed 100% success in determining WBC cells.
Journal ArticleDOI
Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images
TL;DR: This study demonstrates classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells and provides the comparison of traditional image processing approach and deep learning methods for classification.
Journal ArticleDOI
Deep learning approach to peripheral leukocyte recognition.
TL;DR: This paper takes leukocyte recognition as object detection task and applies two remarkable object detection approaches, Single Shot Multibox Detector and An Incremental Improvement Version of You Only Look Once to improve recognition performance.
References
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Book
Neural Networks And Learning Machines
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
Journal ArticleDOI
Automatic measurement of sister chromatid exchange frequency.
G W Zack,W E Rogers,S A Latt +2 more
TL;DR: An automatic system for detecting and counting sister chromatid exchanges in human chromosomes has been developed and showed satisfactory agreement up to levels of approximately 30 sister chromatsid exchanges/cell, or slightly more than twice control levels.
Proceedings ArticleDOI
Morphological classification of blood leucocytes by microscope images
Vincenzo Piuri,Fabio Scotti +1 more
TL;DR: This paper presents a methodology to achieve an automated detection and classification of leucocytes by microscope color images and firstly individuates in the blood image the leucocyte from the others blood cells, then it extracts morphological indexes and finally it classifies the leukocytes by a neural classifier in Basophil, Eosinophils, Lymphocyte, Monocyte and Neutrophil.
Proceedings ArticleDOI
Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images
TL;DR: The presented paper shows the effectiveness of an automatic morphological method to identify the Acute Lymphocytic Leukemia by peripheral blood microscope images.