[PDF][PDF] A systematic literature review of deep and machine learning algorithms in brain tumor and meta-analysis

AMH Taha, D Ariffin, SS Abu-Naser - Journal of Theoretical and Applied …, 2023 - jatit.org
Brain tumor (BT) is considered one of the dangerous conditions that could strike both adults
and children. 85 to 90% of all primary malignancies of the “Central Nervous System (CNS)” …

[HTML][HTML] Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm

MK Islam, MS Ali, MS Miah, MM Rahman… - Machine Learning with …, 2021 - Elsevier
In the present era, human brain tumor is the extremist dangerous and devil to the human
being that leads to certain death. Furthermore, the brain tumor arises more complexity of …

Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach

PMS Raja - Biocybernetics and Biomedical Engineering, 2020 - Elsevier
In medical image processing, brain tumor detection and segmentation is a challenging and
time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in …

[HTML][HTML] Differential deep convolutional neural network model for brain tumor classification

I Abd El Kader, G Xu, Z Shuai, S Saminu, I Javaid… - Brain Sciences, 2021 - mdpi.com
The classification of brain tumors is a difficult task in the field of medical image analysis.
Improving algorithms and machine learning technology helps radiologists to easily diagnose …

A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor

KV Archana, G Komarasamy - Journal of Intelligent Systems, 2023 - degruyter.com
In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In
the medical industry, MRI images are commonly used to analyze and diagnose tumor …

[HTML][HTML] Brain tumor detection and classification on MR images by a deep wavelet auto-encoder model

I Abd El Kader, G Xu, Z Shuai, S Saminu, I Javaid… - diagnostics, 2021 - mdpi.com
The process of diagnosing brain tumors is very complicated for many reasons, including the
brain's synaptic structure, size, and shape. Machine learning techniques are employed to …

Brain tumor segmentation of mri images using processed image driven u-net architecture

A Arora, A Jayal, M Gupta, P Mittal, SC Satapathy - Computers, 2021 - mdpi.com
Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an
essential step in diagnosis and treatment planning to maximize the likelihood of successful …

Fully automated brain tumour segmentation system in 3D‐MRI using symmetry analysis of brain and level sets

A Kermi, K Andjouh, F Zidane - IET Image Processing, 2018 - Wiley Online Library
This study presents a new fully automated, fast, and accurate brain tumour segmentation
method which automatically detects and extracts whole tumours from 3D‐MRI. The …

A comparative study for brain tumor detection in MRI images using texture features

PK Bhagat, P Choudhary, KM Singh - Sensors for health monitoring, 2019 - Elsevier
MRI images are a very rich source of texture. Analysis of texture is crucial for the automation
of tumor detection from MRI images. By analyzing what information a specific texture …

[PDF][PDF] Brain tumor classification using adaptive neuro-fuzzy inference system from MRI

S Roy, S Sadhu, SK Bandyopadhyay… - … Journal of Bio …, 2016 - researchgate.net
Detecting correct type of brain tumor is a crucial task for diagnosis and curing the tumor.
Identifying the correct type of brain tumor can provide a fast and effective way to plan the …