对比多种优化器在宫颈癌筛查模型中的表现，发现Adamax效果最佳。
New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture
- 采用迁移学习+7种优化器，比较其在宫颈涂片图像分类中的表现。
- Adamax在VGG-16和ResNet-18上分别达到72.8%和66.8%准确率，最优。
- 研究为医学影像智能诊断的模型配置提供实用参考。
巴氏涂片是早期宫颈癌筛查的重要方法。卷积神经网络（CNN）中优化器的选择对图像分类性能至关重要，尤其在宫颈癌巴氏涂片图像分类任务中。本研究使用SipakMed数据集，测试了随机梯度下降（SGD）、RMSprop、Adam、AdaGrad、AdaDelta、Adamax和Nadam等七种优化器，结合ResNet-18、ResNet-34和VGG-16三种CNN架构，并均采用迁移学习策略。实验结果表明，迁移学习在所有模型和优化器下均表现更优，且优化器对训练影响较小。其中，Adamax在VGG-16和ResNet-18上的准确率分别为72.8%和66.8%，优于其他优化器；ResNet-34准确率为54.0%，较Nadam低0.034%。总体而言，Adamax在三种架构上均为较优选择。该研究为宫颈癌筛查中CNN模型的配置提供了新见解。
原文摘要 · Abstract (English)
The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), RMSprop, Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
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