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DOI: 10.14569/IJACSA.2020.0110848
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A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Author 1: Sunita M. Kulkarni
Author 2: G. Sundari

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.

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Abstract: The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

Keywords: Brain MRI; segmentation; CNN; deep learning; transfer learning

Sunita M. Kulkarni and G. Sundari, “A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110848

@article{Kulkarni2020,
title = {A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110848},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110848},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Sunita M. Kulkarni and G. Sundari}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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