Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging
M Arabahmadi, R Farahbakhsh, J Rezazadeh - Sensors, 2022 - mdpi.com
Advances in technology have been able to affect all aspects of human life. For example, the
use of technology in medicine has made significant contributions to human society. In this …
use of technology in medicine has made significant contributions to human society. 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 …
COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet
Background Currently, there is an urgent need for efficient tools to assess the diagnosis of
COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling …
COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling …
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 …
Enhanced region growing for brain tumor MR image segmentation
ES Biratu, F Schwenker, TG Debelee, SR Kebede… - Journal of …, 2021 - mdpi.com
A brain tumor is one of the foremost reasons for the rise in mortality among children and
adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces …
adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces …
A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images
Several techniques are used to detect brain tumors in the medical research field; however,
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
[HTML][HTML] Advances in medical image segmentation: a comprehensive review of traditional, deep learning and hybrid approaches
Y Xu, R Quan, W Xu, Y Huang, X Chen, F Liu - Bioengineering, 2024 - mdpi.com
Medical image segmentation plays a critical role in accurate diagnosis and treatment
planning, enabling precise analysis across a wide range of clinical tasks. This review begins …
planning, enabling precise analysis across a wide range of clinical tasks. This review begins …
Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing …
Grape yield forecasting is a valuable economic and quality issue for the grape and wine
industry. The number of flowers at bloom could be used as an early indicator towards crop …
industry. The number of flowers at bloom could be used as an early indicator towards crop …
Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model
The automatic and accurate detection and segmentation of brain tumors is a very tedious
and challenging task for medical experts and radiologists. This paper proposes a hybrid …
and challenging task for medical experts and radiologists. This paper proposes a hybrid …
Brain tumor segmentation from 3d MRI scans using U-NeT
A fully automated system based on three-dimensional (3D) magnetic resonance imaging
(MRI) scans for brain tumor segmentation could be a diagnostic aid to clinical specialists, as …
(MRI) scans for brain tumor segmentation could be a diagnostic aid to clinical specialists, as …