Download PDFOpen PDF in browserImproving Classification with CNNs using Wavelet Pooling with Nesterov-Accelerated Adam10 pages•Published: March 18, 2019AbstractWavelet pooling methods can improve the classification accuracy of Convolutional Neural Networks (CNNs). Combining wavelet pooling with the Nesterov-accelerated Adam (NAdam) gradient calculation method can improve both the accuracy of the CNN. We have implemented wavelet pooling with NAdam in this work using both a Haar wavelet (WavPool-NH) and a Shannon wavelet (WavPool-NS). The WavPool-NH and WavPool- NS methods are most accurate of the methods we considered for the MNIST and LIDC- IDRI lung tumor data-sets. The WavPool-NH and WavPool-NS implementations have an accuracy of 95.92% and 95.52%, respectively, on the LIDC-IDRI data-set. This is an improvement from the 92.93% accuracy obtained on this data-set with the max pooling method. The WavPool methods also avoid overfitting which is a concern with max pool- ing. We also found WavPool performed fairly well on the CIFAR-10 data-set, however, overfitting was an issue with all the methods we considered. Wavelet pooling, especially when combined with an adaptive gradient and wavelets chosen specifically for the data, has the potential to outperform current methods.Keyphrases: biomedical imaging, cancer detection, clinical decision support systems, computational and artificial intelligence, convolutional neural networks, deep learning, healthcare informatics, medical tests In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 84-93.
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