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8 November 2023 A method for esophageal cancer classification using mutual information feature selection and adversarial samples
Lu Bai, Ge Zhang
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Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292323 (2023) https://doi.org/10.1117/12.3011459
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China
Abstract
Early detection and treatment of esophageal cancer may improve the survival rate of patients, despite its high incidence and mortality. The use of computer technology can assist in the diagnosis of esophageal cancer. RNA-Seq gene expression data can be used for the diagnosis of esophageal cancer, but it is difficult to analyze directly because of its high dimension and small sample size. Applying computer technology to this data can solve these problems. In our work, we used the RNA-Seq gene expression dataset and considered the specificity of the sample, proposed an artificial intelligence approach for esophageal cancer classification through selecting the comprehensive features of RNA-Seq gene expression data using mutual information feature selection and obtaining a set of sample specific features by generating adversarial examples using one-pixel attack method to reduce the dimensionality of the dataset. Finally, the deep learning method is used to construct a deep neural network as the classifier. The experimental results reveal that this method outperforms other state-of-the-art algorithms in terms of accuracy and other metrics.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Lu Bai and Ge Zhang "A method for esophageal cancer classification using mutual information feature selection and adversarial samples", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292323 (8 November 2023); https://doi.org/10.1117/12.3011459
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KEYWORDS
Cancer
Feature selection
Neural networks
Data modeling
Deep learning
Artificial intelligence
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