ZFNet and deep Maxout network based cancer prediction using gene expression data

Abstract

The earlier predictions of cancer types are highly essential. Currently, gene expression data (GED) is employed for effective and earlier diagnosing of cancer. The GED allows the models to effectively learn the problem and acts as the most efficient strategy for extracting relevant and new data. Various researchers have implemented many techniques for cancer prediction but the accuracy is required to be improved for the early prediction. Here, the Zeiler and Fergus network fused Deep Maxout Network (ZF-maxout Net) is designed for cancer prediction using GED. At first, input GED data is taken from a certain dataset. Then, data transformation is performed in input GED utilizing Box-Cox transformation. After that, feature fusion (FF) is conducted using the Deep Neural Network (DNN) with Kulczynski similarity. Finally, the cancer prediction is done by utilizing ZF-maxout Net. Moreover, ZF-maxout Net is an amalgamation of the Zeiler and Fergus network (ZFNet) and Deep Maxout Network (DMN). In addition, ZF-mCanceraxout Net obtained a maximal accuracy of 92.7 %, a True Positive Rate (TPR) of 95.8 % and a minimal False Negative Rate (FNR) of 49.9 %.