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

Magendiran N¹, Karthik R², Dhanalakshmi V³

¹Department of Computer Science and Technology, Vivekanandha College of Engineering for Women (Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering

|May 20, 2025

Summary

This summary is machine-generated.

This study introduces a modified Quantum Dilated Convolutional neural network (QDCNN) for accurate cancer detection using gene expression data, achieving 90.6% accuracy. The model effectively predicts cancer, offering a promising advancement in computational oncology.

Keywords:

Gene expression data Kulczynski adaptive Box-Cox transformation angular separation distance feature fusion

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Area of Science:

Computational biology
Bioinformatics
Machine learning in healthcare

Background:

Gene expression data is crucial for understanding cancer biology.
Accurate cancer detection from gene expression data remains a challenge.
Deep learning models show potential in analyzing complex biological datasets.

Purpose of the Study:

To propose a modified Quantum Dilated Convolutional neural network (QDCNN) for enhanced cancer detection.
To leverage gene expression data for improved diagnostic accuracy.
To evaluate the performance of the proposed QDCNN model on a cancer dataset.

Main Methods:

Utilized gene expression data from the PANCAN dataset.
Applied Adaptive Box-Cox transformation for data preprocessing.
Employed a Deep Neural Network (DNN) with Kulczynski for feature fusion.
Fed refined features into a modified QDCNN for cancer prediction.

Main Results:

The modified QDCNN achieved an accuracy of 90.6%.
Achieved a True Positive Rate (TPR) of 89.0%.
Reported a False Negative Rate (FNR) of 0.109 and a Matthews correlation coefficient (MCC) of 89.9%.

Conclusions:

The modified QDCNN demonstrates high efficacy in detecting cancer from gene expression data.
The proposed method offers a significant improvement in cancer prediction accuracy.
This approach holds potential for clinical applications in cancer diagnostics.