The most typical way of identifying and counting red and white blood cells involves manual operations on a microscope, which are set up by a laboratory worker with their own experience. We will create a computer programme to recognise and identify the recommended objects based on their pattern in this study. In terms of cell detection, our proposed method is quite effective. Red Blood Cells (RBCs) and White Blood Cells (WBCs) are the proposed objects (WBCs). An approach proposed by Viola and Jones will be used to identify and classify blood cells. To improve the accuracy of the learning algorithm’s error, the Adaboost (adaptive boosting) method will be used. By displaying the number and time spent by cells discovered, the output of the suggested programme demonstrates that all of the categories of cells stated can be detected and classified properly.
Department of Statistics, Universitas Hamzanwadi, Lombok, Indonesia.
Department of Mathematics, Mahidol University, Bangkok, Thailand.
Center of Medical Laboratory Service, Mahidol University, Bangkok, Thailand.
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