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Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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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Abbreviations

LGG:

Low-grade glioma

HGG:

High-grade glioma

MRI:

Magnetic resonance imaging

CT:

Computed tomography

DT :

Decision tree

LDA:

Linear discriminant analysis

PNN:

Probabilistic neural network

SSAE:

Stacked sparse auto encoder

NN:

Neural networks

MPSO:

Modified particle swarm optimization

PTPSA:

Piece-wise triangular prism surface area

FCM :

Fuzzy C-means

KNN:

k-nearest neighbor

ABC:

Artificial Bee Colony

CNN’s:

Convolutional neural networks

X:

Input image

H:

Hidden Layer

C:

Kernel vector

\( {\hat{\upvarphi}}_{\mathrm{c}} \) :

Average activation value

B:

bias

ε :

Activation module

δ :

Mean squared reconstruction error

AE:

Autoencoder

ρ :

Weight decay module

γ :

Sparsity penalty

Φ:

Sparsity parameter

Ω:

Controls weight

∅:

Softmax layer

ACC:

Accuracy

SE :

Sensitivity

SP:

Specificity

DSC:

Dice similarity coefficient

JSI:

Jaccard similarity index

ROC :

Receiver operating characteristic curve

JSI :

Jaccard similarity index

FPR :

False positive rate

FNR:

False negative rate

HPF:

High pass filter

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Javaria Amin, Muhammad Sharif, Mudassar Raza & Muhammad Wasif Nisar

  2. Department of radiology, Wah Medical College, POF Hospital, Wah Cantt, Rawalpindi, Punjab, Pakistan

    Nadia Gul

  3. College of EME, NUST, Rawalpindi, Punjab, Pakistan

    Muhammad Almas Anjum

  4. Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, USA

    Syed Ahmad Chan Bukhari

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  1. Javaria Amin
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  2. Muhammad Sharif
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  3. Nadia Gul
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Correspondence to Muhammad Sharif.

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Amin, J., Sharif, M., Gul, N. et al. Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning. J Med Syst 44, 32 (2020). https://doi.org/10.1007/s10916-019-1483-2

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