Detection of subtype blood cells using deep learning |
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Institution: | 1. School of Computer Science and Technology, Hangzhou Dianzi University, China;2. School of Media and Design, Hangzhou Dianzi University, China;3. Department of Orthopaedic Surgery, Sir Run Run Shaw Hospital, China |
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Abstract: | Deep Learning has already shown power in many application fields, and is accepted by more and more people as a better approach than the traditional machine learning models. In particular, the implementation of deep learning algorithms, especially Convolutional Neural Networks (CNN), brings huge benefits to the medical field, where a huge number of images are to be processed and analyzed. This paper aims to develop a deep learning model to address the blood cell classification problem, which is one of the most challenging problems in blood diagnosis. A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells. Experiments are conducted on a dataset of 13k images of blood cells with their subtypes, and the results show that our proposed model provide better results in terms of evaluation parameters. |
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Keywords: | Blood cells Classification CNN |
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