Breast cancer detection using an ensemble deep learning method

In this work, the effectiveness of the deep learning model is applied for one-dimensional data when converted to images. This work is based on the effective conversion of one-dimensional data to images and designing a stacked ensemble deep learning model that can increase the performance of classification accuracy in comparison to single models. Breast cancer detection from gene expression dataset and breast histopathology images is considered using the proposed ensemble model. The gene expression data is one-dimensional. Using the tDistributed Stochastic Neighbor embedding technique and Convex Hull algorithm the one-dimensional data is converted to an image. Existing methods are using the datasets directly for training and classification, whereas the proposed method uses the dataset as well as the decomposed forms of the same for improving the performance. It involves two-stage classification. The first stage consists of three Convolutional Neural Networks as the base classifiers. Empirical Wavelet Transform and Variational Mode Decomposition are the two methods used to decompose the dataset so that the models can be trained at the molecular level, making our model robust in comparison to state-of-the-art methods. The first stage classification outcomes are used to train the second stage classifier "Multilayer Perceptron". The gene expression dataset collected from Mendeley and is used for the generation of two-dimensional synthetic datasets. The synthetic datasets and breast histopathology image datasets are used for the training and validation of the proposedmodel. The improved results obtained in this work show the effectiveness of our method