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Brain tumor detection: a long short-term memory (LSTM)-based learning model

  • Recent Advances in Deep Learning for Medical Image Processing
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Abstract

To overcome the problems of automated brain tumor classification, a novel approach is proposed based on long short-term memory (LSTM) model using magnetic resonance images (MRI). First, N4ITK and Gaussian filters having size 5 × 5 are used to boost the of multi-sequence MRI quality. The presented deep LSTM model having four layers is utilized for classification. In each layer, optimal hidden units (HU) are selected such as 200 HU, 225 HU, 200 HU and 225 HU, respectively. These hidden or concealed units are chosen after performing extensive experiments to acquire better results. The results are validated on different versions of BRATS datasets (BRATS 2012–15, 2018) and SISS-ISLES 2015 dataset. The presented method attained dice similarity coefficient (DSC) 1.00 on 2012 synthetic, 0.95 on 2013, 0.99 on 2013 Leader board, 0.99 on 2014, 0.98 on 2015, 0.99 on 2018 and 0.95 on SISS-ISLES 2015. The methodology is also checked on real patient’s cases of brain tumor collected from Pakistan ordinance factory and achieved 0.97 DSC. The results confirm that the presented method provides more help for radiologists to classify brain tumor precisely.

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Abbreviations

\( x_{\text{t}} \) :

Input image

\( h_{t} \) :

Hidden layer

\( O_{t} \) :

Model output

B :

Bias

c t :

Cell state time step

R :

Recurrent weights

\( \varSigma \) :

Sigmoid activation function

\( \odot \) :

Hadamard product

RNN:

Recurrent neural network

NNs:

Feedforward neural networks

SE:

Sensitivity

SP:

Specificity

FN:

False negative

FP:

False positive

TN:

True negative

TP:

True positive

DSC:

Dice similarity coefficient

DWI:

Diffusion-weighted imaging

FNR:

False negative rate

FLAIR:

Fluid-attenuated inversion recovery

T1c:

T1-weighted contrast-enhanced

T1:

T1-weighted

RF:

Random forests

SVMs:

Support vector machines

CNNs:

Convolutional neural networks

MRFs:

Markov random fields

CEN:

Convolutional encoder networks

\( {\text{HGG}} \) :

High-grade glioma

CRFs:

Conditional random fields

\( {\text{LGG}} \) :

Low-grade glioma

KNN:

K-nearest neighbor

DT:

Decision trees

MRI:

Magnetic resonance images

JSI:

Jaccard similarity index

\( {\text{FPR}} \) :

False positive rate

PPV:

Positive predictive value

ACC:

Accuracy

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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

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  3. Department of Mathematics, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Rafiq Sial

  4. Department of Computer Science, Luther College, Decorah, IA, USA

    Shafqat Ali Shad

Authors
  1. Javaria Amin
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  2. Muhammad Sharif
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  3. Mudassar Raza
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  4. Tanzila Saba
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  5. Rafiq Sial
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  6. Shafqat Ali Shad
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Correspondence to Muhammad Sharif.

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Amin, J., Sharif, M., Raza, M. et al. Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput & Applic 32, 15965–15973 (2020). https://doi.org/10.1007/s00521-019-04650-7

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  • DOI: https://doi.org/10.1007/s00521-019-04650-7

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