Machine learning approach of automatic identification and counting of blood cells.

Clicks: 278
ID: 46637
2019
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Abstract
A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using 'you only look once' (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.
Reference Key
alam2019machinehealthcare Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alam, Mohammad Mahmudul;Islam, Mohammad Tariqul;
Journal healthcare technology letters
Year 2019
DOI
10.1049/htl.2018.5098
URL
Keywords
counting image classification blood object detection learning (artificial intelligence) medical image processing automatic identification blood cells detection blood smear images cellular biophysics complete blood cell count machine learning approach neural nets patient diagnosis red blood cells white blood cells

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