A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image
The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …
and artificial intelligence conspire in the timely delineation, detection, and classification of …
Multi-modal brain tumor detection using deep neural network and multiclass SVM
Background and Objectives: Clinical diagnosis has become very significant in today's health
system. The most serious disease and the leading cause of mortality globally is brain cancer …
system. The most serious disease and the leading cause of mortality globally is brain cancer …
Human-inspired optimization algorithms: Theoretical foundations, algorithms, open-research issues and application for multi-level thresholding
Humans take immense pride in their ability to be unpredictably intelligent and despite huge
advances in science over the past century; our understanding about human brain is still far …
advances in science over the past century; our understanding about human brain is still far …
A decision support system for multimodal brain tumor classification using deep learning
Multiclass classification of brain tumors is an important area of research in the field of
medical imaging. Since accuracy is crucial in the classification, a number of techniques are …
medical imaging. Since accuracy is crucial in the classification, a number of techniques are …
Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning
Cancer is considered one of the most aggressive and destructive diseases that shortens the
average lives of patients. Misdiagnosed brain tumours lead to false medical intervention …
average lives of patients. Misdiagnosed brain tumours lead to false medical intervention …
A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced Brain tumor Detection and Classification scheme in medical image processing
The major intention of this work is to detect the Brain tumor with accuracy by reducing error
rate and computational complexity. Therefore, in this manuscript, a Deep Convolutional …
rate and computational complexity. Therefore, in this manuscript, a Deep Convolutional …
Efficient framework for brain tumour classification using hierarchical deep learning neural network classifier
In this manuscript, an efficient framework is proposed for brain tumour classification (BTC)
based on hierarchical deep-learning neural network (HieDNN) classifier. Here, the input …
based on hierarchical deep-learning neural network (HieDNN) classifier. Here, the input …
Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation
Image segmentation is a procedure of dividing the digital image into multiple set of pixels.
The intention of the segmentation is to “transform the representation of medical images into …
The intention of the segmentation is to “transform the representation of medical images into …
An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network
Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The
abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to …
abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to …
Centroid mutation-based search and rescue optimization algorithm for feature selection and classification
Massive data is generated as a result of technological innovations in various fields. Medical
data sets often have extremely complex dimensions with limited sample sizes. The …
data sets often have extremely complex dimensions with limited sample sizes. The …