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Detection and classification of white blood cells using convolutional neural networks (CNN)
Convolutional neural networks (CNN)
white blood cell (WBC)
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Blood film examination has many kinds of clinical application, including blood cell classification, blood cell count, and bone marrow examination. From the blood film, we can know the patient's blood condition. In addition, it can help doctors to diagnose the patient's condition and understand the effect of treatment. However, these jobs take a lot of time and labor. Therefore, we hope that the research in this thesis can help them for the white blood cell (WBC) examination. In recent years, deep learning has become popular in machine learning, including the convolutional neural networks (CNN). Convolutional neural networks have good performances on image recognition. In this thesis, we use this method to detect and classify white blood cells. The experiments were conducted by using the dataset that was taken from Taichung Tzu Chi Hospital. In order to find the most effective neural network, we have tried many different neural networks. In addition, we use the Faster R-CNN to help us to detect the white blood cells on the blood film image. Moreover, we also use the Holdout validation method to ensure the credibility of the experimental results, and the probability of the recognition can reach more than 99%.
|Appears in Collections:||通訊工程研究所|
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