Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
During the last decade, computer vision and machine learning have revolutionized the world
in every way possible. Deep Learning is a sub field of machine learning that has shown …
in every way possible. Deep Learning is a sub field of machine learning that has shown …
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
Abstract The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on
global health and the daily life of people still living in more than two hundred countries. The …
global health and the daily life of people still living in more than two hundred countries. The …
Brain tumor classification using dense efficient-net
Brain tumors are most common in children and the elderly. It is a serious form of cancer
caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously …
caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously …
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 …
Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
Brain is the controlling center of our body. With the advent of time, newer and newer brain
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
Brain tumor classification using modified local binary patterns (LBP) feature extraction methods
Automatic classification of brain tumor types is very important for accelerating the treatment
process, planning and increasing the patient's survival rate. Today, MR images are used to …
process, planning and increasing the patient's survival rate. Today, MR images are used to …
[HTML][HTML] Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor
Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment
options, while accurate classification and segmentation of brain tumors are vital for …
options, while accurate classification and segmentation of brain tumors are vital for …
Differential deep convolutional neural network model for brain tumor classification
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 …
Improving algorithms and machine learning technology helps radiologists to easily diagnose …
Artificial intelligence for brain diseases: A systematic review
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …
analyzing complex medical data and extracting meaningful relationships in datasets, for …