Abstract: When using AlexNet to implement image classification of gastric tumor cells, there are problems of small dataset, slow model convergence and low recognition rate. Aiming at the above problems, a Data Augmentation (DA) method based on Radial Transformation (RT) and improved AlexNet was proposed. The original dataset was divided into test set and training set. In the test set, cropping was used to increase the data. In the training set, cropping, rotation, flipping and brightness conversion were employed to obtain the enhanced image set, and then some of them were selected for RT processing to achieve the enhanced effect. In addition, the replacement activation of functions and normalization layers was used to speed up the convergence and improve the generalization performance of AlexNet. Experimental results show that the proposed method can implement the recognition of gastric tumor cell images with faster convergence and higher recognition accuracy. On the test set, the highest accuracy is 99.50% and the average accuracy is 96.69%, and the F1 scores of categories:canceration, normal and hyperplasia are 0.980, 0.954 and 0.958 respectively, indicating that the proposed method can implement the recognition of gastric tumor cell images well.

Key words: small dataset, Data Augmentation (DA), Radial Transformation (RT), Convolutional Neural Network (CNN), gastric tumor cell image recognition

摘要： 使用AlexNet实现胃肿瘤细胞图像分类时，存在数据集过小和模型收敛速度慢、识别率低的问题。针对上述问题，提出基于径向变换（RT）的数据增强（DA）和改进AlexNet的方法。将原始数据集划分为测试集和训练集，测试集采用剪裁方式增加数据，训练集首先采用剪裁、旋转、翻转和亮度变换得到增强图片集；然后选取其中一部分进行RT处理达到增强效果。此外，采用替换激活函数和归一化层的方式提高AlexNet的收敛速度并提高其泛化性能。实验结果表明，所提方法能以较快的收敛速度和较高的识别准确率实现胃肿瘤细胞图像的识别，在测试集中最高准确率为99.50%，平均准确率为96.69%，癌变、正常和增生三个类别的F1值分别为0.980、0.954和0.958，表明该方法较好地实现了胃肿瘤细胞图像的识别。

关键词: 小样本数据集, 数据增强, 径向变换, 卷积神经网络, 胃肿瘤细胞图像识别