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" Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN "


Record Number : 1502344
Language of Document : English
Main Entry : Ouiza Nait Belaid
Title & Author : Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN [electronic resources: essay]/ Ouiza Nait Belaid؛ Malik Loudini
Piece Level : Journal of Information Technology Management
Notes Pertaining to Publication, Distribution, Etc. : Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)
: September 2020
Access Link : https://jitm.ut.ac.ir/article_75788.html
: https://jitm.ut.ac.ir/article_75788_e36c948ee9258c82b9398f136692f3f5.pdf
Summary or Abstract : In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.
Topical Name Used as Subject : Deep learning
: GLCM features
: VGG16 CNN
: Brain tumor
Personal Name - Alternative Intelectual Responsibility : Malik Loudini
Originating Source : University of Tehran. Central Library and Documentation Center
copyhttps://lib.ut.ac.ir/site/catalogue/1502344
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