Paper Info
Title
Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network.
Abstract
Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2902252
IEEE ACCESS
Keywords
DocType
Volume
Neural network (NN),deep neural network (DNN),autoencoder (AE),image classification
Journal
7
ISSN
Citations 
PageRank 
2169-3536
2
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Pradeep Kumar Mallick120.37
Seuc-Ho Ryu220.71
Sandeep Kumar Satapathy361.83
Shruti Mishra421.39
Nhu Gia Nguyen5253.45
Prayag Tiwari64315.01