Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy
S Devunooru, A Alsadoon, PWC Chandana… - Journal of Ambient …, 2021 - Springer
Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be
time-consuming and in most cases, reading of the resulting images by human agents is …
time-consuming and in most cases, reading of the resulting images by human agents is …
MRI brain tumor medical images analysis using deep learning techniques: a systematic review
The substantial progress of medical imaging technology in the last decade makes it
challenging for medical experts and radiologists to analyze and classify. Medical images …
challenging for medical experts and radiologists to analyze and classify. Medical images …
Brain tumor detection using statistical and machine learning method
Abstract Background and Objective Brain tumor occurs because of anomalous development
of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths …
of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths …
Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection
Brain tumor identification using magnetic resonance images (MRI) is an important research
domain in the field of medical imaging. Use of computerized techniques helps the doctors for …
domain in the field of medical imaging. Use of computerized techniques helps the doctors for …
Dual-force convolutional neural networks for accurate brain tumor segmentation
Brain tumor segmentation from Magnetic Resonance Imaging scans is vital for both the
diagnosis and treatment of brain cancers. It is widely accepted that accurate segmentation …
diagnosis and treatment of brain cancers. It is widely accepted that accurate segmentation …
[HTML][HTML] Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques
S Ahuja, BK Panigrahi, TK Gandhi - Machine Learning with Applications, 2022 - Elsevier
The brain tumor is the deadliest disease in adults as it arises due to an abnormal mass of
cells that grows rapidly and it alters the proper functioning of the organs. In clinical practice …
cells that grows rapidly and it alters the proper functioning of the organs. In clinical practice …
Heterogeneous oblique random forest
R Katuwal, PN Suganthan, L Zhang - Pattern Recognition, 2020 - Elsevier
Decision trees in random forests use a single feature in non-leaf nodes to split the data.
Such splitting results in axis-parallel decision boundaries which may fail to exploit the …
Such splitting results in axis-parallel decision boundaries which may fail to exploit the …
A Tri-Attention fusion guided multi-modal segmentation network
In the field of multimodal segmentation, the correlation between different modalities can be
considered for improving the segmentation results. Considering the correlation between …
considered for improving the segmentation results. Considering the correlation between …
Ensemble machine learning approaches for webshell detection in Internet of things environments
Abstract The Internet of things (IoT), made up of a massive number of sensor devices
interconnected, can be used for data exchange, intelligent identification, and management …
interconnected, can be used for data exchange, intelligent identification, and management …
Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks
Fully convolutional networks have been achieving remarkable results in image semantic
segmentation, while being efficient. Such efficiency results from the capability of segmenting …
segmentation, while being efficient. Such efficiency results from the capability of segmenting …