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A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 996))

Abstract

Segmentation of brain tumor is a very crucial task from the medical points of view,  such as in surgery and treatment planning. The tumor can be noticeable at any region of the brain with various size and shape due to its nature, that makes the segmentation task more difficult. In this present work, we propose a patch-based automated segmentation of brain tumor using a deep convolutional neural network with small convolutional kernels and leaky rectifier linear units (LReLU) as an activation function. Present work efficiently segments multi-modalities magnetic resonance (MR) brain images into normal and tumor tissues. The presence of small convolutional kernels allow more layers to form a deeper architecture and less number of the kernel weights in each layer during training. Leaky rectifier linear unit (LReLU) solves the problem of rectifier linear unit (ReLU) and increases the speed of the training process. The present work can deal with both high- and low-grade tumor regions on MR images. BraTS 2015 dataset has been used in the present work as a standard benchmark dataset. The presented network takes T1, T2, T1c, and FLAIR MR images from each subject as inputs and produces the segmented labels as outputs. It is experimentally observed that the present work has obtained promising results than the existing algorithms depending on the ground truth.

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Acknowledgements

This work is supported by the Board of Research in Nuclear Sciences (BRNS), DAE, Government of India under the Reference No. 34/14/13/2016-BRNS/34044.

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Authors and Affiliations

  1. RCC Institute of Information Technology, Kolkata, India

    Abhishek Bal & Minakshi Banerjee

  2. Apollo Gleneagles Hospital, Kolkata, India

    Punit Sharma

  3. A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India

    Rituparna Chaki

Authors
  1. Abhishek Bal
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  2. Minakshi Banerjee
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  3. Punit Sharma
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  4. Rituparna Chaki
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Corresponding author

Correspondence to Abhishek Bal .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Computer Science, DAIS, Università Ca’ Foscari, Mestre, Venice, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Bal, A., Banerjee, M., Sharma, P., Chaki, R. (2020). A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-8969-6_6

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