NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION
NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION
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用于肿瘤研究和癌症预测的神经网络分析
DOI：
10.36548/jes.2019.2.004
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Vijayakumar T
中科院分区：
文献类型：
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作者：
Vijayakumar T
在肿瘤的早期阶段预测肿瘤的种类和癌症的类型仍然是确定疾病的深度和治疗方法的一个非常重要的过程，神经网络具有类似于人类神经系统的功能，被广泛用于肿瘤的调查和癌症的预测。本文分析了前馈神经网络（FNN）、递归神经网络（RNN）和卷积神经网络（CNN）等神经网络在肿瘤研究和癌症预测中的性能。通过对乳腺癌威斯康星州原始数据集的神经网络进行评估所获得的结果表明，CNN的预测效果比FNN好43%，比RNN好25%。
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.