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Maas, A.L., Hannun, A.Y. and Ng, A.Y. (2013) Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the 30th International Conference on Machine Learning, Vol. 28, 3.
https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf

has been cited by the following article:

• TITLE: A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network

  AUTHORS: Ahmad M. Sarhan

  KEYWORDS: Convolutional Neural Network CNN), Wavelet Transform, Image Classification, Lung Cancer, Computerized Tomography (CT)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.13 No.5, May 28, 2020

  ABSTRACT: Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.