论文标题
检查最先进的卷积神经网络的行为，以进行和不转移学习
Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learning
论文作者
论文摘要
区分正常和确定肿瘤类型是脑肿瘤诊断的关键成分。在这项研究工作中，使用最先进的CNN模型研究了两种不同类型的数据集。一个数据集（二进制）具有正常和肿瘤类型的图像，而另一个数据集则提供了分类为神经胶质瘤，脑膜瘤或垂体的所有肿瘤图像。实验是在这些数据集中进行的，并通过从图像网的预训练重量进行转移学习，并随机初始化权重。实验环境对于本研究中的所有模型都是等效的，以进行公平的比较。对于两个数据集，对于所有火车数据均为60％而其余的验证的模型的验证集均相同。通过这项研究中提出的技术，有效网络B5体系结构的表现优于二进制分类数据集中的所有最新模型，其精度为99.75％，多级数据集的精度为98.61％。这项研究还证明了不同权重初始化技术中验证损失的融合行为。
Distinguishing normal from malignant and determining the tumor type are critical components of brain tumor diagnosis. Two different kinds of dataset are investigated using state-of-the-art CNN models in this research work. One dataset(binary) has images of normal and tumor types, while another(multi-class) provides all images of tumors classified as glioma, meningioma, or pituitary. The experiments were conducted in these dataset with transfer learning from pre-trained weights from ImageNet as well as initializing the weights randomly. The experimental environment is equivalent for all models in this study in order to make a fair comparison. For both of the dataset, the validation set are same for all the models where train data is 60% while the rest is 40% for validation. With the proposed techniques in this research, the EfficientNet-B5 architecture outperforms all the state-of-the-art models in the binary-classification dataset with the accuracy of 99.75% and 98.61% accuracy for the multi-class dataset. This research also demonstrates the behaviour of convergence of validation loss in different weight initialization techniques.
