Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

https://doi.org/10.22146/ijeis.34713

Wijang Widhiarso(1*), Yohannes Yohannes(2), Cendy Prakarsah(3)

(1) STMIK Global Informatika MDP Palembang
(2) STMIK Global Informtika MDP PAlembang
(3) STMIK Global Informatika MDP Palembang
(*) Corresponding Author

Abstract


Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.


Keywords


Gray Level Co-occurrence Matrix; Convolutional Neural Network; Brain Tumor Classification; Meningioma; Glioma; Pituitary Tumor

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DOI: https://doi.org/10.22146/ijeis.34713

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