神经网络分析在肿瘤调查和肿瘤预测中的应用

T. Vijayakumar
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引用次数: 54

摘要

在早期阶段预测肿瘤的类别和癌症的类型仍然是确定疾病深度和可用治疗方法的一个非常重要的过程。神经网络具有与人类神经系统相似的功能,在肿瘤研究和肿瘤预测中得到了广泛应用。本文分析了前馈神经网络(FNN)、循环神经网络(RNN)和卷积神经网络(CNN)等神经网络对肿瘤的研究和预测的性能。通过对乳腺癌Wisconsin原始数据集的神经网络进行评估得到的结果表明,CNN的预测效果比FNN好43%,比RNN好25%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION
Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.
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