摘要
针对临床医学中急性淋巴细胞白血病(Acute Lymphoblastic Leukemia,ALL)血液细胞显微图像分类易错、费时等问题,本文提出了一种基于深度学习VGG16卷积神经网络模型的方法去获取医学图像中高纬度的病理信息。该方法首先将样本数据进行预处理,清洗出符合要求的训练集和验证集,其中还用到了超像素的方法用于训练样本的目标区域提取,然后再将预处理好的数据,输入到VGG16卷积神经网络模型中,对其进行训练,最后输入验证集进入模型中进行验证,实验结果表明,该分类方法能有效地完成ALL血液细胞显微图像是否患病的分类。
Aiming at the problem of accurate classification and time consuming in clinical medicine microscopic images of acute lymphoblastic leukemia(ALL), this paper proposed a method based on deep learning VGG16 convolutional neural network model to obtain pathological information at high latitudes in medical images. In the paper, we firstly preprocessed the sample, cleaned out the training set and verification set that met the requirements, and also used the super-pixel method to extract the target area of the training sample. Then we trained VGG16 network by inputting the preprocessed data, and finally the validation set was entered into the model for verification. The experimental result showed that the classification method could effectively complete the classification of ALL blood cell microscopic images.
引文
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