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A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

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

Abstract

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.

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Abbreviations

∇:

Sharp edges

⊗:

Convolutional

ε:

Smoothing

Si :

Resultant image

T :

Threshold

\( \mathcal{R} \), :

Opening

λ:

Erosion

Ѱ:

Dilation

L:

Layer

F:

Kernels bank

S:

Stride

CC:

Channel

Col:

Column

β(Inputi):

Softmax

N:

Number of layers

FInput :

Kernel vector of ith neuron

e :

Probability

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Authors and Affiliations

  1. Department of Computer Science, University of Wah, Wah, Pakistan

    Javeria Amin

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

    Javeria Amin, Muhammad Sharif & Mussarat Yasmin

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Tanzila Saba

  4. College of EME, NUST Pakistan, Islamabad, Pakistan

    Muhammad Almas Anjum

  5. Department of Electronics and Communication Engineering, Sahyadri College of Engineering & Management, Mangaluru, India

    Steven Lawrence Fernandes

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  1. Javeria Amin
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Correspondence to Javeria Amin.

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Amin, J., Sharif, M., Yasmin, M. et al. A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning. J Med Syst 43, 326 (2019). https://doi.org/10.1007/s10916-019-1453-8

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