[HTML][HTML] 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 …
An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification
S Mascarenhas, M Agarwal - 2021 International conference on …, 2021 - ieeexplore.ieee.org
Artificial Intelligence advancements have come a long way over the past twenty years. Rapid
developments in AI have given birth to a trending topic called machine learning. Machine …
developments in AI have given birth to a trending topic called machine learning. Machine …
[HTML][HTML] A deep analysis of brain tumor detection from mr images using deep learning networks
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 …
Detection and segmentation of loess landslides via satellite images: A two-phase framework
Landslides are catastrophic natural hazards that often lead to loss of life, property damage,
and economic disruption. Image-based landslide investigations are crucial for determining …
and economic disruption. Image-based landslide investigations are crucial for determining …
[HTML][HTML] Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures
Medical imaging refers to visualization techniques to provide valuable information about the
internal structures of the human body for clinical applications, diagnosis, treatment, and …
internal structures of the human body for clinical applications, diagnosis, treatment, and …
[HTML][HTML] Transfer learning in magnetic resonance brain imaging: A systematic review
(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …
Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network
Melanoma is the most fatal type of skin cancer which can cause the death of victims at the
advanced stage. Extensive work has been presented by the researcher on computer vision …
advanced stage. Extensive work has been presented by the researcher on computer vision …
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 …
[HTML][HTML] An efficient optimization technique for training deep neural networks
Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a
neural network. It has many applications in the field of banking, automobile industry …
neural network. It has many applications in the field of banking, automobile industry …