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Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network
Gehad Abdullah Amran
, Mohammed Shakeeb Alsharam
, Abdullah Omar A. Blajam
, Ali A. Hasan
, Mohammad Y. Alfaifi
, Mohammed H. Amran
,
Abdu Gumaei
, Sayed M. Eldin
Computer Sciences
Dalian University of Technology
Al-Razi University
Tianjin Agricultural University
Zhejiang Normal University
Northeastern University China
King Khalid University
Ogarev Mordovia State University
Future University in Egypt
Research output
:
Contribution to journal
›
Article
›
peer-review
76
Scopus citations
Overview
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Computer Science
Key Performance Indicator
100%
Convolutional Neural Network
100%
Early Detection
100%
Machine Learning
50%
Learning System
50%
Machine Learning Algorithm
50%
Deep Learning Method
50%
Classification Performance
50%
Residual Neural Network
50%
Transfer Learning
50%
Extracted Feature
50%
VGG-16
50%
Manual Segmentation
50%
Segmentation Technique
50%
Performance Measure
50%
Engineering
Key Performance Indicator
100%
Convolutional Neural Network
100%
Early Detection
100%
Deep Learning Method
50%
Learning System
50%
Machine Learning Algorithm
50%
Extracted Feature
50%
Transfer Learning
50%
Classification Performance
50%
Hybrid Method
50%
Segmentation Technique
50%
Periodic Time
50%
Biochemistry, Genetics and Molecular Biology
Tumor Classification
100%
Magnetic Resonance Imaging
60%
Transfer of Learning
20%
Lifespan
20%
Neuroscience
Intracranial Tumor
100%
Magnetic Resonance Imaging
33%
Machine Learning Algorithm
11%
Brain Cancer
11%