AI in medical imaging informatics: current challenges and future directions
This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …
imaging informatics, discusses clinical translation, and provides future directions for …
A review on brain tumor segmentation of MRI images
The process of segmenting tumor from MRI image of a brain is one of the highly focused
areas in the community of medical science as MRI is noninvasive imaging. This paper …
areas in the community of medical science as MRI is noninvasive imaging. This paper …
Brain tumor classification using convolutional neural network
N Abiwinanda, M Hanif, ST Hesaputra… - World Congress on …, 2019 - Springer
Misdiagnosis of brain tumor types will prevent effective response to medical intervention and
decrease the chance of survival among patients. One conventional method to differentiate …
decrease the chance of survival among patients. One conventional method to differentiate …
BrainNet: optimal deep learning feature fusion for brain tumor classification
Early detection of brain tumors can save precious human life. This work presents a fully
automated design to classify brain tumors. The proposed scheme employs optimal deep …
automated design to classify brain tumors. The proposed scheme employs optimal deep …
Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
G Karayegen, MF Aksahin - Biomedical Signal Processing and Control, 2021 - Elsevier
When it comes to medical image segmentation on brain MR images, using deep learning
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
Combining deep and handcrafted image features for MRI brain scan classification
Progresses in the areas of artificial intelligence, machine learning, and medical imaging
technologies have allowed the development of the medical image processing field with …
technologies have allowed the development of the medical image processing field with …
[HTML][HTML] DenseUNet+: A novel hybrid segmentation approach based on multi-modality images for brain tumor segmentation
Segmentation of brain tumors is of great importance for patients in clinical diagnosis and
treatment. For this reason, experts try to identify border regions of special importance using …
treatment. For this reason, experts try to identify border regions of special importance using …
ResUNet+: A new convolutional and attention block-based approach for brain tumor segmentation
The number of brain tumor cases has increased in recent years. Therefore, accurate
diagnosis and treatment of brain tumors are extremely important. Accurate detection of tumor …
diagnosis and treatment of brain tumors are extremely important. Accurate detection of tumor …
A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions
S Krishnapriya, Y Karuna - Health and Technology, 2023 - Springer
Abstract Purpose Structural Magnetic Resonance Imaging (MRI) of the brain is an effective
way to study its internal structure. Identifying and classifying brain malignancies is a difficult …
way to study its internal structure. Identifying and classifying brain malignancies is a difficult …
Adaptive independent subspace analysis of brain magnetic resonance imaging data
Methods for image registration, segmentation, and visualization of magnetic resonance
imaging (MRI) data are used widely to help medical doctors in supporting diagnostics. The …
imaging (MRI) data are used widely to help medical doctors in supporting diagnostics. The …