论文标题
使用转移学习的深度神经网络对脑肿瘤图像的多分类
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network
论文作者
论文摘要
在最近对基于计算机的诊断系统的进步中，脑肿瘤图像的分类是一项艰巨的任务。本文主要着重于提高基于转移学习的深神经网络的脑肿瘤图像的分类精度。分类方法是从图像增强操作开始的，包括旋转，变焦，Hori-Zontal Flip，宽度偏移，高度移位和剪切，以增加图像数据集中的多样性。然后，根据由Inception-V3组成的预训练的转移学习方法提取输入脑肿瘤图像的一般特征。 fi-nally，使用4个定制层的深神经网络用于将大多数脑肿瘤类型的脑肿瘤与脑膜瘤，神经胶质瘤和垂体进行分类。提出的模型以96.25％的总体准确性获得了有效性能，这比某些现有的多分类方法得到了更大的提高。鉴于，超参数的微调以及具有Inception-V3模型的定制DNN的包含导致分类精度的IM提供。
In recent advancement towards computer based diagnostics system, the classification of brain tumor images is a challenging task. This paper mainly focuses on elevating the classification accuracy of brain tumor images with transfer learning based deep neural network. The classification approach is started with the image augmentation operation including rotation, zoom, hori-zontal flip, width shift, height shift, and shear to increase the diversity in image datasets. Then the general features of the input brain tumor images are extracted based on a pre-trained transfer learning method comprised of Inception-v3. Fi-nally, the deep neural network with 4 customized layers is employed for classi-fying the brain tumors in most frequent brain tumor types as meningioma, glioma, and pituitary. The proposed model acquires an effective performance with an overall accuracy of 96.25% which is much improved than some existing multi-classification methods. Whereas, the fine-tuning of hyper-parameters and inclusion of customized DNN with the Inception-v3 model results in an im-provement of the classification accuracy.
