标题： SLENet：一种用于检测大鼠发情周期的新型多尺度CNN网络 显示英文标题

标题： SLENet: A Novel Multiscale CNN-Based Network for Detecting the Rats Estrous Cycle

摘要： 在临床医学中，大鼠常被用作实验对象。 然而，其发情周期显著影响其生物反应，导致实验结果的差异。 因此，准确确定发情周期对于减少干扰至关重要。 手动识别大鼠的发情周期存在多种挑战，包括高成本、长培训时间以及主观性。 为解决这些问题，本文提出了一种分类网络——空间长距离高效网络（SLENet）。 该网络基于EfficientNet设计，具体通过引入一种新的空间高效通道注意力（SECA）机制来替换原始的挤压激励（SE）模块，对移动倒置瓶颈卷积（MBConv）模块进行了修改。 此外，在最后一个卷积层之后引入了非局部注意力机制，以增强网络捕捉长距离依赖关系的能力。 使用的数据集包含2,655张大鼠阴道上皮细胞的显微图像，测试集中有531张图像。 实验结果表明，SLENet的准确率达到96.31%，优于基线EfficientNet模型（94.2%）。 这一发现为优化基于大鼠的研究如生殖和药理学研究的实验设计提供了实际价值，但本研究仅限于显微图像数据，未考虑其他因素如时间模式，因此，未来应用中需要结合多模态输入。 显示英文摘要

摘要： In clinical medicine, rats are commonly used as experimental subjects. However, their estrous cycle significantly impacts their biological responses, leading to differences in experimental results. Therefore, accurately determining the estrous cycle is crucial for minimizing interference. Manually identifying the estrous cycle in rats presents several challenges, including high costs, long training periods, and subjectivity. To address these issues, this paper proposes a classification network-Spatial Long-distance EfficientNet (SLENet). This network is designed based on EfficientNet, specifically modifying the Mobile Inverted Bottleneck Convolution (MBConv) module by introducing a novel Spatial Efficient Channel Attention (SECA) mechanism to replace the original Squeeze Excitation (SE) module. Additionally, a Non-local attention mechanism is incorporated after the last convolutional layer to enhance the network's ability to capture long-range dependencies. The dataset used 2,655 microscopic images of rat vaginal epithelial cells, with 531 images in the test set. Experimental results indicate that SLENet achieved an accuracy of 96.31%, outperforming baseline EfficientNet model (94.2%). This finding provide practical value for optimizing experimental design in rat-based studies such as reproductive and pharmacological research, but this study is limited to microscopy image data, without considering other factors like temporal patterns, thus, incorporating multi-modal input is necessary for future application.