Microarray Cancer Data Classification using Deep LearningMethods

Abstract

A proposal is made in this paper regarding the deep feed-forward neural network for the microarray binary dataset’s classification. We have used eight binary class standard datasets of microarray cancer used the purpose of validating the suggested approach, specifically cancers of the brain, colon, prostate, leukemia, ovary, lung-Harvard2, lung-Michigan, and breast.In addition, six multiclass microarray datasets namely 3-class Leukemia, 4-class Leukemia,4-class SRBCT, 3-class MLL, 5-class Lung cancer and 11-class Tumor are alsoconsidered.To come out with curse of dimensionality, the method for reducing dimensionality is PCA in binary class dataset’s case. We have crafted architecture of neural network which is fully connected, configuring its parameters with sigmoid initialization for network's input and hidden layer. This includes specifying the number of epochs, batch sizes, and selecting appropriate activation functions. The suggested method's multiclass behavior is made possible by initializing the activation function SoftMax to the output layer. The min-max approach is used for featurescaling.To compute the magnitude of error of the method,binary cross-entropy, and categorical cross-entropy are used on the binary and multi-class datasets and the ADAM optimizer is for optimization.A study is conducted to compare the suggested approach with the most advanced techniques available. According to experimental findings on these common microarray datasets and comparisons with the most advanced technique, the suggested method's performance is quite respectable.