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 …
Brain image segmentation in recent years: A narrative review
Brain image segmentation is one of the most time-consuming and challenging procedures in
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
A deep analysis of brain tumor detection from mr images using deep learning networks
MI Mahmud, M Mamun, A Abdelgawad - Algorithms, 2023 - mdpi.com
Creating machines that behave and work in a way similar to humans is the objective of
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
An advanced machine learning based energy management of renewable microgrids considering hybrid electric vehicles' charging demand
Renewable microgrids are new solutions for enhanced security, improved reliability and
boosted power quality and operation in power systems. By deploying different sources of …
boosted power quality and operation in power systems. By deploying different sources of …
Timedistributed-cnn-lstm: A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study
Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely
diagnosis can increase the chances of survival. Considering the challenges of tumor …
diagnosis can increase the chances of survival. Considering the challenges of tumor …
Automated detection of brain tumor through magnetic resonance images using convolutional neural network
Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues.
Therefore, early and accurate detection of this disease can save patient's life. This paper …
Therefore, early and accurate detection of this disease can save patient's life. This paper …
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 …
HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection
Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if
not cured at an early stage. In spite of several promising results and substantial efforts in this …
not cured at an early stage. In spite of several promising results and substantial efforts in this …
Training of the feed forward artificial neural networks using dragonfly algorithm
Ş Gülcü - Applied Soft Computing, 2022 - Elsevier
One of the most important parts of an artificial neural network (ANN) which affects
performance is training algorithms. Training algorithms optimize the weights and biases of …
performance is training algorithms. Training algorithms optimize the weights and biases of …
Tumor localization and classification from MRI of brain using deep convolution neural network and Salp swarm algorithm
Early diagnosis of brain tumors is crucial for treatment planning and increasing the survival
rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and …
rates of infected patients. In fact, brain tumors exist in a range of different forms, sizes, and …