Open Access
ARTICLE
An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach
Ravi Samikannu1, *, Rohini Ravi2, Sivaram Murugan3, Bakary Diarra4
1 Faculty of Electrical Engineering, Botswana International University of Science and Technology, Palapye, Botswana.
2 Faculty of Computer Science Engineering, Vivekanandha College of Engineering for Women, Namakkal, 637205, India.
3 Department of Computer Networking, Lebanese French University, Erbil, 44001, Iraq.
4 Institute of Applied Sciences, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali.
* Corresponding Author: Ravi Samikannu. Email: .
Computers, Materials & Continua 2020, 63(3), 1133-1142. https://doi.org/10.32604/cmc.2020.08578
Received 10 September 2019; Accepted 12 January 2020; Issue published 30 April 2020
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.
Keywords
Cite This Article
R. Samikannu, R. Ravi, S. Murugan and B. Diarra, "An efficient image analysis framework for the classification of glioma brain images using cnn approach,"
Computers, Materials & Continua, vol. 63, no.3, pp. 1133–1142, 2020. https://doi.org/10.32604/cmc.2020.08578
Citations