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

PubMed
Authors: N M, R K, V D, S S

Year

2026

Paper ID

9668

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

96

Citations

1

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • This paper proposes a modified Quantum Dilated Convolutional neural network (QDCNN) to detect cancer using gene expression data.

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External citation index: OpenAlex citation signal • updated 2026-06-13 22:50:41

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