In the proposed DLR, a convolutional neural network (CNN) is used. CNN is a representative method used for deep learning, and it has been successfully applied to the field of image segmentation8. Recently, many groups have used CNN for the segmentation of medical images, and it has provided better results than traditional meth- ods9. In glioma segmentation based on MR images, most of the CNN methods were proposed for high-grade gliomas10, 11. Compared with high-grade gliomas, low-grade gliomas are smaller and have lower contrast with the surrounding tissues12. Existing CNN structures would not work well for segmentation of low-grade gliomas. A major architecture adjustment of CNN is therefore essential for both image segmentation and feature extraction. To address the challenging characteristics of low-grade gliomas, we used a modified CNN architecture with 6 convolutional layers and a fully connected layer with 4096 neurons for segmentation. 解释

这段文字讲述了一种名为DLR的分割方法，其中使用了卷积神经网络（CNN），这是一种应用于深度学习的代表性方法，并且已经成功地应用于图像分割领域。最近，许多团队已经将CNN用于医学图像分割，并且相比传统方法，CNN方法提供了更好的结果。然而，对于低级别胶质瘤的分割，由于其大小较小且与周围组织对比度较低，现有的CNN结构无法很好地工作。因此，需要进行主要的架构调整来进行图像分割和特征提取。为了解决低级别胶质瘤的挑战性特征，作者使用了修改后的CNN架构，其中包括6个卷积层和一个有4096个神经元的全连接层用于分割。

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Ma et al. (2021) used ResNet-50+FPN(He et al. 2016; Lin et al. 2017) to carry a semantic segmentation neural network, demonstrating the feasibility of deep learning in large-scale AGs mapping. Chen et al. (2021) successfully extracted AGs with the help of the classic semantic segmentation network UNet, and since then, some segmentation models specifically designed for AGs mapping tasks have been proposed(He et al. 2023; Liu et al. 2023). Although these models are based on classical convolutional neural networks (CNNs) and improved with the help of advanced components in CNNs to achieve better results, there are still three main problems in AGs mapping: difficult to extract spatially dense distribution, algorithm maladaptation, and lack of trainable data. On the other hand, the intrinsic relationship between the visual features of AGs and the network architecture has not been sufficiently explained. How to implement an efficient AGs segmentation model based on the unique or more niche characteristics of AGs still needs to be supplemented more.

Ma等人（2021）采用ResNet-50+FPN（He等人2016；Lin等人2017）构建了一个语义分割神经网络，展示了深度学习在大规模农田地块映射中的可行性。陈等人（2021）成功地利用经典的语义分割网络UNet提取了农田地块，并且此后还提出了一些专门针对农田地块映射任务设计的分割模型（He等人2023；Liu等人2023）。尽管这些模型以经典卷积神经网络（CNNs）为基础，并借助CNNs中的先进组件做出改进取得了更好的效果，但农田地块映射仍存在三个主要问题：难以提取空间密集分布、算法不适应性以及缺少可训练数据。另一方面，农田地块视觉特征与网络架构之间的内在关系还没有得到充分的解释。如何基于农田地块的独特或更专业的特性来实现高效的农田地块分割模型，仍需要更多的补充。

The conventional convolution neural network (CNN) adopts softmax function as classifier, which has problems of overflow and underflow. This paper proposes a rolling bearing intelligent fault diagnosis method based on multi-scale convolution neural network, bi-directional long short term memory and support vector machine (MCNN-BiLSTM-SVM). The wavelet threshold denoising algorithm is adopted for signal preprocessing. The multi-scale convolution neural network (MCNN) and the bidirectional long short-term memory network (BiLSTM) are combined as the feature extractor to improve feature extraction capability. The support vector machine (SVM) is adopted as the classifier to improve classification performance. Transfer learning is used in MCNN-BiLSTM-SVM for different conditions. According to the experiments, the proposed MCNN-BiLSTM-SVM fault diagnosis method has higher diagnostic accuracy, stronger anti-noise performance and better stability under different conditions than other diagnostic methods.给出以上内容审稿意见

本文提出了一种基于多尺度卷积神经网络、双向长短时记忆网络和支持向量机的轴承智能故障诊断方法（MCNN-BiLSTM-SVM）。该方法采用小波阈值去噪算法进行信号预处理，将多尺度卷积神经网络（MCNN）和双向长短时记忆网络（BiLSTM）组合作为特征提取器，以提高特征提取能力；采用支持向量机（SVM）作为分类器，以提高分类性能。在MCNN-BiLSTM-SVM中使用迁移学习处理不同条件下的数据。经过实验验证，本文提出的MCNN-BiLSTM-SVM故障诊断方法具有更高的诊断准确性、更强的抗噪性能和更好的稳定性，优于其他诊断方法。

该论文在轴承智能故障诊断方面提出了一种新的方法，并且通过实验证明其有效性。同时，论文的结构清晰，表述准确，实验数据充分且有说服力。建议作者进一步说明MCNN-BiLSTM-SVM方法在处理不同条件下的数据时，采用了哪些具体的迁移学习方法，以便读者更好地理解。此外，建议作者在文献综述中加入更多相关领域的研究工作，以进一步突显本文的创新性和实用性。