Brain tumor detection and classification using machine learning: a comprehensive survey
J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022 - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of
deaths among kids and adults from the past few years. According to WHO standard, the …
deaths among kids and adults from the past few years. According to WHO standard, the …
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 …
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 …
time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in …
Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions
The physical appearance of a brain tumor in human beings may be an indication of
problems in psychological (cognitive) functions. Such functions include learning …
problems in psychological (cognitive) functions. Such functions include learning …
Deep radiomics for brain tumor detection and classification from multi-sequence MRI
Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG
and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to …
and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to …
Automated brain tumor segmentation based on multi-planar superpixel level features extracted from 3D MR images
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance
for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is …
for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is …
Multi-planar spatial-ConvNet for segmentation and survival prediction in brain cancer
A new deep learning method is introduced for the automatic delineation/segmentation of
brain tumors from multi-sequence MR images. A Radiomic model for predicting the Overall …
brain tumors from multi-sequence MR images. A Radiomic model for predicting the Overall …
Brain tumor classification using deep convolutional autoencoder-based neural network: Multi-task approach
F Bashir-Gonbadi, H Khotanlou - Multimedia tools and applications, 2021 - Springer
Diagnosis, detection and classification of tumors, in the brain MRI images, are important
because misdiagnosis can lead to death. This paper proposes a method that can diagnose …
because misdiagnosis can lead to death. This paper proposes a method that can diagnose …
Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review
Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …
addressed with algorithm-based segmentation tools. In this study, we map the field of …