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 …
Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision
S Asif, Y Wenhui, S ur-Rehman, Q ul-ain… - … Methods in Engineering, 2024 - Springer
Abstract Machine learning (ML) has emerged as a versatile and powerful tool in various
fields of medicine, revolutionizing early disease diagnosis, particularly in cases where …
fields of medicine, revolutionizing early disease diagnosis, particularly in cases where …
Brain tumor detection based on deep learning approaches and magnetic resonance imaging
Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification
The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term
damage to the brain. Magnetic resonance imaging (MRI) is one of the most common …
damage to the brain. Magnetic resonance imaging (MRI) is one of the most common …
Optimizing the topology of convolutional neural network (CNN) and artificial neural network (ANN) for brain tumor diagnosis (BTD) through MRIs
The use of MRI analysis for BTD and tumor type detection has considerable importance
within the domain of machine vision. Numerous methodologies have been proposed to …
within the domain of machine vision. Numerous methodologies have been proposed to …
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 …
[HTML][HTML] Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
Brain tumors, characterized by abnormal cell growth, pose a significant challenge in clinical
imaging due to their complex and diverse structures. Early and accurate identification …
imaging due to their complex and diverse structures. Early and accurate identification …
Deep learning based semantic segmentation approach for automatic detection of brain tumor
S Markkandeyan, S Gupta, GV Narayanan… - International Journal of …, 2023 - univagora.ro
Initially, fromBRATS 2013 dataset the input image is acquired and is preprocessed,
segmented using Convolutional neural network (CNN) based semantic segmentation, and …
segmented using Convolutional neural network (CNN) based semantic segmentation, and …
Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework
CS DS, J Christopher Clement - Scientific Reports, 2024 - nature.com
Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise
from rapidly multiplying cells. During medical imaging, it is essential to separate brain …
from rapidly multiplying cells. During medical imaging, it is essential to separate brain …
NDNN based U-Net: An innovative 3D brain tumor segmentation method
Identifying and segmenting brain tumors using multi-sequence 3D volumetric MRI scans is
time-consuming and challenging. Deep learning-based automatic image segmentation …
time-consuming and challenging. Deep learning-based automatic image segmentation …