@Article{cmc.2020.08578,
AUTHOR = {Ravi Samikannu, Rohini Ravi, Sivaram Murugan, Bakary Diarra},
TITLE = {An Efficient Image Analysis Framework for the Classification of  Glioma Brain Images Using CNN Approach},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1133--1142},
URL = {http://www.techscience.com/cmc/v63n3/38866},
ISSN = {1546-2226},
ABSTRACT = {The identification of brain tumors is multifarious work for the separation of the 
similar intensity pixels from their surrounding neighbours. The detection of tumors is 
performed with the help of automatic computing technique as presented in the proposed 
work. The non-active cells in brain region are known to be benign and they will never 
cause the death of the patient. These non-active cells follow a uniform pattern in brain and 
have lower density than the surrounding pixels. The Magnetic Resonance (MR) image 
contrast is improved by the cost map construction technique. The deep learning algorithm 
for differentiating the normal brain MRI images from glioma cases is implemented in the 
proposed method. This technique permits to extract the linear features from the brain MR 
image and glioma tumors are detected based on these extracted features. Using k-mean 
clustering algorithm the tumor regions in glioma are classified. The proposed algorithm 
provides high sensitivity, specificity and tumor segmentation accuracy.},
DOI = {10.32604/cmc.2020.08578}
}