基于深度卷积神经网络的白细胞分类

Kan Throngnumchai, Pitchayakorn Lomvisai, Chayanan Tantasirin, P. Phasukkit
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引用次数: 9

摘要

白细胞是免疫系统的一种,参与保护身体免受感染疾病和外来入侵者的侵害。白细胞有不同的种类,每一种类都能反映机体的不规则性。目前,白细胞诊断通常由医生手工检查。与计算机自动化加工相比,该工艺耗时长、成本高、易出错。提出了一种以新鲜血液涂片[1]图像为中心的显微白细胞图像自动分类技术。本文提出了一种基于深度卷积神经网络(DCNN)的分类方法。对1万张显微血液图像进行测试,分类方法获得93%
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Classification of White blood cell using Deep Convolutional Neural Network
White blood cells are the one of immune system that are involved in protecting the body against infection disease and foreign invaders. There are difference category of white blood cell and each category can indicate about the irregularity of body. Nowadays, White blood cell diagnosis is usually examined manually by doctor. This process consumes a lot time, cost and susceptible to error compare with automatic computerize process. An automatic classification technique for microscopic white blood cell images focusing on images from fresh blood smears[1] is proposed in this paper. The classification is conducted using a proposed method that consist of deep convolutional neural network (DCNN). 10,000 Microscopic blood images were tested and the classification method obtain 93%
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