Open AccessA Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO
Author(s) -
T. Anitha,
G. Charlyn Pushpa Latha,
Surendra Prasad M
Publication year - 2020
Publication title -
journal of computational science and intelligent technologies
Language(s) - English
Resource type - Journals
ISSN - 2582-9041
DOI - 10.53409/mnaa.jcsit20201302
Subject(s) - artificial intelligence , computer science , support vector machine , convolutional neural network , segmentation , brain tumor , pattern recognition (psychology) , cluster analysis , artificial neural network , medicine , pathology
Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.