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 …
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Multi-modal brain tumor detection using deep neural network and multiclass SVM
S Maqsood, R Damaševičius, R Maskeliūnas - Medicina, 2022 - mdpi.com
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 …
Brain tumor/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model
With the advancement in technology, machine learning can be applied to diagnose the
mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a …
mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a …
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 …
Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …
neural network, residual neural network, adversarial network; U-Net architectures are most …
Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors
The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …
major role in accurate diagnosis and treatment planning. The present study proposes a new …
Deep learning for brain tumor segmentation: a survey of state-of-the-art
Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
[HTML][HTML] Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN
N Kesav, MG Jibukumar - Journal of King Saud University-Computer and …, 2022 - Elsevier
Abstract The Brain Tumor is one of the most serious scenarios associated with the brain
where a cluster of abnormal cells grows in an uncontrolled fashion. The field of image …
where a cluster of abnormal cells grows in an uncontrolled fashion. The field of image …
Artificial intelligence surgery: how do we get to autonomous actions in surgery?
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery.
Interestingly, many examples of autonomous actions already exist and have been around for …
Interestingly, many examples of autonomous actions already exist and have been around for …