Automated Brain Tumor Detection in MRI Images Using Efficient Deep Learning Methods
P. Tamije Selvy1, M.Anitha2

1P.Tamije Selvy, Department of Computer Science and Engineering, Sri Krishna College of Technology / Coimbatore, India
2M.Anitha, Department of Computer Science and Engineering, Sri Krishna College of Technology / Coimbatore, India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 12 September 2019. | Manuscript published on 30 September 2019. | PP: 286-291 | Volume-8 Issue-11, September 2019. | Retrieval Number: K13240981119/2019©BEIESP | DOI: 10.35940/ijitee.K1324.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Brain tumor is an unusual intensification of cells inside the skull. The brain MRI scanned images is segmented to extract brain tumor to analyze type and depth of tumor. In order to reduce the time consumption of brain tumor extraction, an automatic method for detection of brain tumor is highly recommended. Deep machine learning methods are used for automatic detection of the brain tumor in soft tissues at an early stage which involves the following stages namely: image pre-processing, clustering and optimization. This paper addresses previously adduced pre-processing (Skull stripping, Contrast stretching, clustering (k-Means, Fuzzy c-means) and optimization (Cuckoo search optimization, Artificial Bee Colony optimization) strategies for abnormal brain tumor detection from MRI brain images. Performance evaluation is done based on computational time of clustering output and optimization algorithms are analyzed in terms of sensitivity, specificity, and accuracy.
Keywords: Cerebro spinal fluid (CSF), Computer aided diagnosis (CAD), Magnetic resonance imaging (MRI), Skull stripping.
Scope of the Article: