Modified quantum dilated convolutional neural network for cancer prediction using gene expression data

This paper proposes a modified Quantum Dilated Convolutional neural network (QDCNN) to detect cancer using gene expression data. Primarily, the input gene expression data is taken from a specified dataset. Then, data transformation is done using Adaptive Box-Cox transformation and feature fusion is done by a Deep Neural Network (DNN) with Kulczynski. The refined features are then fed into the modified QDCNN, which effectively predicts cancer. The modified QDCNN attains an accuracy of 90.6%, a True Positive Rate (TPR) of 89.0%, False Negative Rate (FNR) of 0.109, and a Matthews correlation coefficient (MCC) of 89.9% when using the PANCAN dataset.