Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

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 detecti...

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Veröffentlicht in:IEEE access Jg. 7; S. 46278 - 46287
Hauptverfasser: Kumar Mallick, Pradeep, Ryu, Seuc Ho, Satapathy, Sandeep Kumar, Mishra, Shruti, Nguyen, Gia Nhu, Tiwari, Prayag
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
autoencoder (AE)
Biological neural networks
Biomedical imaging
Brain
Classifiers
deep neural network (DNN)
Image classification
Image compression
Image processing
Image segmentation
Magnetic resonance imaging
Medical imaging
Medical science
Neural network (NN)
Neural networks
Task analysis
Tumors
Wavelet transforms
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2902252