论文标题
基于人造神经网络的乳腺癌筛查：全面评论
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
论文作者
论文摘要
乳腺癌是女性的常见致命疾病。为了改善受乳腺癌影响的人的预后，早期诊断和检测是必要的。为了预测乳腺癌，已经使用不同的医学成像方式开发了几种自动化系统。本文对基于人工神经网络（ANN）模型的文献进行了系统的综述，以通过乳腺X线摄影诊断乳腺癌。在本次审查中描述了不同ANN模型的优点和局限性，包括尖峰神经网络（SNN），深信念网络（DBN），卷积神经网络（CNN），多层神经网络（MLNN），堆叠的自动编码器（SAE）和堆叠的DeNoing Autoencoders（SDAE）。该评论还表明，与乳腺癌检测有关的研究将不同的深度学习模型应用于许多公开可用的数据集。为了比较模型的性能，在现有研究中使用了不同的指标，例如准确性，精度，召回等。发现最佳性能是通过残留神经网络（RESNET）-50和RESNET-101 CNN算法模型实现的。
Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people. For predicting breast cancer, several automated systems are already developed using different medical imaging modalities. This paper provides a systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography. The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked de-noising autoencoders (SDAE) are described in this review. The review also shows that the studies related to breast cancer detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies. It is found that the best performance was achieved by residual neural network (ResNet)-50 and ResNet-101 models of CNN algorithm.
