Research Article Open Access

A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

Ramzi Ben Ali1, Ridha Ejbali1 and Mourad Zaied1
  • 1 University of Gabes, Tunisia


This work present a new solution for medical image classification using the Neural Network (NN) and Wavelet Network (WN) based on the Fast Wavelet Transform (FWT) and the Adaboost algorithm. This method is divided in two stages: The learning stage and the classification stage. The first consists to extract the features using the FWT based on the MultiResolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Then, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. The second consist to create an AutoEncoder (AE) using the best-selected wavelets of all images. Then, after a series of Stacked AE, a pooling is applied for each hidden layer to get our Convolutional Deep Neural Wavelet Network (CDNWN) architecture for the classification phase. Our experiments were performed on two different datasets and the obtained classifications rates given by our approach show a clear improvement compared to those cited in this article.

Journal of Computer Science
Volume 14 No. 11, 2018, 1488-1498


Submitted On: 31 July 2018 Published On: 11 November 2018

How to Cite: Ali, R. B., Ejbali, R. & Zaied, M. (2018). A Deep Convolutional Neural Wavelet Network for Classification of Medical Images. Journal of Computer Science, 14(11), 1488-1498.

  • 6 Citations



  • Adaboost
  • Deep Convolutional Neural Wavelets Network
  • Images Classification
  • Fast Wavelet Transform
  • Pattern Recognition