Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm

Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-9
Year of Publication : 2020
Authors : Narendra Mohan
DOI :  10.14445/22315381/IJETT-V68I9P214

Citation 

MLA Style: Narendra Mohan "Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm" International Journal of Engineering Trends and Technology 68.9(2020):84-100. 

APA Style:Narendra Mohan. Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm  International Journal of Engineering Trends and Technology, 68(9),84-100.

Abstract
Major cause for high mortality in human being is brain tumor. Improper and delayed treatment leads to the development of malignant tumor which is untreatable. This realize us the necessity of tumor detection at earlier stage. For such early detection, initially the skull removal process is carried out in input MRI using Brain Surface Extraction (BSE) technique. The lesion enhancement process over the skull removed image is performed using Weiner filter. It is performed to attain better segmentation result. Next, the tumor region is segmented from the non-tumor part using region growing segmentation approach. Features are required to recognize whether the segmented tumor is benign or malignant. Therefore, SGLDM and LESH based feature extraction approaches are used in this method. The dimensionality of extracted features is reduced using feature selection process. Finally with that selected features the tumor classification is achieved using the MSLO based KELM approach. The effectiveness of proposed KELM-MSLO approach is determined using the benchmark datasets such as BRATS 2013 Leader board, BRATS 2014, 2015, and 2018. Finally, some performance metrics are evaluated to analyze the effective performance of presented technique on detection of tumor at former stage.

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Keywords
Brain Tumor, Segmentation, Kernel Extreme Learning Machine (KELM), Skull Removal, Region-Growing Technique.