Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities...

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Bibliographic Details
Published in:Journal of medical systems Vol. 44; no. 2; p. 32
Main Authors: Amin, Javaria, Sharif, Muhammad, Gul, Nadia, Raza, Mudassar, Anjum, Muhammad Almas, Nisar, Muhammad Wasif, Bukhari, Syed Ahmad Chan
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2020
Springer Nature B.V
Subjects:
Brain cancer
Brain tumors
Deep learning
Glioma
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Image & Signal Processing
Medicine
Medicine & Public Health
Recent Advances in Deep Learning for Biomedical Signal Processing
Statistics for Life Sciences
Tumors
Softmax
Hidden size
Glioma
Stacked sparse autoencoder
Magnetic resonance images
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary:Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices’ intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1483-2