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Lung cancer classification using deep learned features on low population dataset | IEEE Conference Publication | IEEE Xplore

Lung cancer classification using deep learned features on low population dataset


Abstract:

Classification of lung cancer using a low population, high dimensional dataset is challenging due to insufficient samples to learn an accurate mapping among features and ...Show More

Abstract:

Classification of lung cancer using a low population, high dimensional dataset is challenging due to insufficient samples to learn an accurate mapping among features and class labels. Current literature usually handles this task through hand-crafted feature creation and selection. In recent years, deep learning is found to be able to identify the underlying structure of data through the use of autoencoders and other techniques. In this work, a deep autoencoder classification mechanism is proposed which first learns deep features and then trains an artificial neural network with these learned features. Experimental results show the deep learned classifier outperforms all other classifiers when trained with all attributes and same training samples. It is also demonstrated that the performance improvement is statistically significant.
Date of Conference: 30 April 2017 - 03 May 2017
Date Added to IEEE Xplore: 15 June 2017
ISBN Information:
Conference Location: Windsor, ON, Canada

References

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