Abstract
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.
Highlights
The deep learning is a set of algorithms of machine learning, seeking to model with the abstractions of top level within the data using the architectures of models composed of multiple not linear transformations
In the art of feature extraction, of a Deep Convolutional Neural Network (DCNN) based on MultiResolution Analysis (MRA) and the Adaboost algorithm
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
Summary
The deep learning is a set of algorithms of machine learning, seeking to model with the abstractions of top level within the data using the architectures of models composed of multiple not linear transformations. We present a Deep Convolutional Neural Network (DCNN) that models images This architecture was based on the MRA at different abstraction levels in order to extract all features. In the art of feature extraction, of a DCNN based on MRA and the Adaboost algorithm It has allowed the modeling of all image features with a one hidden layer. FWT is used to extract features based on convolutional dyadic MRA analysis on different levels of abstraction as shown in Fig. 6 in the first step. FWT is used to extract the feature based on MRA at different levels This technique accelerates the calculation of the weights of connection (Jemai et al, 2011). Using the best contribution algorithm, we construct a wavelet network for each element of a class (Zaied et al, 2011; Jemai et al, 2010) of wavelets Ψi∈D as shown in the following Fig. 3
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