深度学习算法在乳腺肿瘤诊断中的应用研究

Application of Deep Learning Algorithms in Diagnosis of Breast Cancer

为探索应用深度学习的三种网络结构对乳腺癌肿瘤的诊断的诊断价值,本文基于深度神经网络、卷积神经网络和循环神经网络(Recurrent Neural Network,RNN)三种基本网络对乳腺肿瘤的良性与恶性的不同进行分类建模,利用人体实际乳腺肿瘤样本数据进行模型参数训练,并利用测试集数据对模型进行验证,结果发现三种网络都能以较高的准确度识别出肿瘤良恶性,其中RNN实验准确度接近100%。该研究可以辅助医生提高乳腺肿瘤的诊断准确率和工作效率。

The purpose of this article is to explore the diagnostic value of three network structures using deep learning for breast cancer tumors.Based on the three basic networks of deep neural network,convolutional neural network and recurrent neural network(RNN),this article models and classifies the difference between benign and malignant breast tumors.Furthermore,the actual breast tumor sample data of the human body was used for model parameter training,and the test set data was used to verify the model.The results showed that the three networks can identify the nature of the tumor in high accuracy,among them the accuracy of RNN was close to 100%.This research can assist doctors in improving the accuracy and efficiency of breast tumor diagnosis.