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 …
A survey on cancer detection via convolutional neural networks: Current challenges and future directions
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
[HTML][HTML] Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard
and important tasks for several applications in the field of medical analysis. As each brain …
and important tasks for several applications in the field of medical analysis. As each brain …
A new algorithm for digital image encryption based on chaos theory
In recent decades, image encryption, as one of the significant information security fields, has
attracted many researchers and scientists. However, several studies have been performed …
attracted many researchers and scientists. However, several studies have been performed …
ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition
Breast tumor segmentation and recognition from mammograms play a key role in healthcare
and treatment services. As different tumors in mammography have dissimilar densities …
and treatment services. As different tumors in mammography have dissimilar densities …
Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods
Abstract The Global Cancer Statistics 2020 reported breast cancer (BC) as the most
common diagnosis of cancer type. Therefore, early detection of such type of cancer would …
common diagnosis of cancer type. Therefore, early detection of such type of cancer would …
MRFE-CNN: Multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network
R Ranjbarzadeh, N Tataei Sarshar… - Annals of Operations …, 2023 - Springer
Breast cancer is cancer that develops from the breast tissue and has been recognized as
one of the most dangerous and deadly diseases that is the second leading cause of cancer …
one of the most dangerous and deadly diseases that is the second leading cause of cancer …
Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images
R Ranjbarzadeh… - BioMed Research …, 2021 - Wiley Online Library
The COVID‐19 pandemic is a global, national, and local public health concern which has
caused a significant outbreak in all countries and regions for both males and females …
caused a significant outbreak in all countries and regions for both males and females …
Nerve optic segmentation in CT images using a deep learning model and a texture descriptor
The increased intracranial pressure (ICP) can be described as an increase in pressure
around the brain and can lead to serious health problems. The assessment of ultrasound …
around the brain and can lead to serious health problems. The assessment of ultrasound …
TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach
Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
applications, such as postoperative assessment, surgical planning, and pathological …
applications, such as postoperative assessment, surgical planning, and pathological …