Brain tumor classification using modified kernel based softplus extreme learning machine

2021 ◽  
Author(s):  
V. V. S. Sasank ◽  
S. Venkateswarlu
Keyword(s):  
Brain Tumor ◽  
Extreme Learning Machine ◽  
Tumor Classification ◽  
Learning Machine

scholarly journals A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 36266-36273 ◽  
Author(s):  
Abdu Gumaei ◽  
Mohammad Mehedi Hassan ◽  
Md Rafiul Hassan ◽  
Abdulhameed Alelaiwi ◽  
Giancarlo Fortino
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Extreme Learning Machine ◽  
Extraction Method ◽  
Tumor Classification ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction ◽  
Learning Machine

Classification of Primary and Secondary Brain Tumor Using Extreme Learning Machine

2021 ◽  
Author(s):  
Romi Fadillah Rahmat ◽  
Riyan Maria Wijaya ◽  
Sharfina Faza ◽  
Erna Budhiarti Nababan ◽  
Farhad Nadi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Extreme Learning Machine ◽  
Learning Machine

scholarly journals A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
K. Kothavari ◽  
B. Arunadevi ◽  
S. N. Deepa
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Extreme Learning Machine ◽  
System Analysis ◽  
Medical Diagnostics ◽  
Magnetic Resonance Images ◽  
Diagnostic Process ◽  
3D Mri ◽  
Learning Machine

Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM) and run-length matrix (RLM) and feature subselection for dimensionality reduction is performed with basic differential evolution (DE) algorithm. Classification is performed using proposed extreme learning machine (ELM), with refined group search optimizer (RGSO) technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories.

scholarly journals Brain Tumor Classification with Hybrid Algorithm Convolutional Neural Network-Extreme Learning Machine

2021 ◽  
Vol 3 (1) ◽  
pp. 29-33
Author(s):  
Radical Rakhman Wahid ◽  
Fetty Tri Anggraeni ◽  
Budi Nugroho
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Hybrid Algorithm ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Average Value ◽  
Learning Machine ◽  
Hidden Layer

Brain tumor is a disease that attacks the brains of living things in which brain cells grow abnormally in the area around the brain. Various ways have been done to detect this disease, one of which is through the anatomical approach to medical images. In this study, the authors propose a Convolutional Neural Network (CNN)-Extreme Learning Machine (ELM) hybrid algorithm through Magnetic Resonance Imaging (MRI). ELM was chosen because of its superiority in the training process, which is faster than iterative machine learning algorithms, while CNN was chosen to replace the traditional feature extraction process. The result is CNN-ELM, which has 8 filters in the convolution layer and 6000 nodes in the hidden layer, has the best performance compared to CNN-ELM another model which has different number of filters and number of nodes in the hidden layer. This is evidenced by the average value of precision, recall, and F1-score which is 0.915 while the accuracy of the test is 91.4%.

L2,1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification

2020 ◽  
Vol 89 ◽  
pp. 107368
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Rong Zhu ◽  
Xiang-Zhen Kong
Keyword(s):  
Extreme Learning Machine ◽  
Tumor Classification ◽  
Learning Machine

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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