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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Published in:	IEEE access Vol. 7; pp. 46278 - 46287
Main Authors:	Kumar Mallick, Pradeep, Ryu, Seuc Ho, Satapathy, Sandeep Kumar, Mishra, Shruti, Nguyen, Gia Nhu, Tiwari, Prayag
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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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.
AbstractList	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.
Author	Ryu, Seuc Ho Mishra, Shruti Kumar Mallick, Pradeep Tiwari, Prayag Satapathy, Sandeep Kumar Nguyen, Gia Nhu
Author_xml	– sequence: 1 givenname: Pradeep surname: Kumar Mallick fullname: Kumar Mallick, Pradeep organization: Department of Game Design, Kongju National University, Gongju, South Korea – sequence: 2 givenname: Seuc Ho surname: Ryu fullname: Ryu, Seuc Ho organization: Department of Game Design, Kongju National University, Gongju, South Korea – sequence: 3 givenname: Sandeep Kumar surname: Satapathy fullname: Satapathy, Sandeep Kumar organization: Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, India – sequence: 4 givenname: Shruti surname: Mishra fullname: Mishra, Shruti organization: Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, India – sequence: 5 givenname: Gia Nhu orcidid: 0000-0003-4267-3900 surname: Nguyen fullname: Nguyen, Gia Nhu email: nguyengianhu@duytan.edu.vn organization: Duy Tan University, Da Nang, Vietnam – sequence: 6 givenname: Prayag orcidid: 0000-0002-2851-4260 surname: Tiwari fullname: Tiwari, Prayag organization: Department of Information Engineering, University of Padova, Padua, Italy
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SubjectTerms	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
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Title	Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network
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