An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review

S Das, GK Nayak, L Saba, M Kalra, JS Suri… - Computers in biology and …, 2022 - Elsevier
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …

[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions

B Pandey, DK Pandey, BP Mishra… - Journal of King Saud …, 2022 - Elsevier
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Brain tumor segmentation with deep convolutional symmetric neural network

H Chen, Z Qin, Y Ding, L Tian, Z Qin - Neurocomputing, 2020 - Elsevier
Gliomas are the most frequent primary brain tumors, which have a high mortality. Surgery is
the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful …

A sequential machine learning-cum-attention mechanism for effective segmentation of brain tumor

TM Ali, A Nawaz, A Ur Rehman, RZ Ahmad… - Frontiers in …, 2022 - frontiersin.org
Magnetic resonance imaging is the most generally utilized imaging methodology that
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …

Application of convolutional neural network in segmenting brain regions from MRI data

HM Ali, MS Kaiser, M Mahmud - International conference on brain …, 2019 - Springer
Extracting knowledge from digital images largely depends on how well the mining
algorithms can focus on specific regions of the image. In multimodality image analysis …

MRI brain tumor medical images analysis using deep learning techniques: a systematic review

SAY Al-Galal, IFT Alshaikhli, MM Abdulrazzaq - Health and Technology, 2021 - Springer
The substantial progress of medical imaging technology in the last decade makes it
challenging for medical experts and radiologists to analyze and classify. Medical images …

Automated meningioma segmentation in multiparametric MRI: comparable effectiveness of a deep learning model and manual segmentation

KR Laukamp, L Pennig, F Thiele, R Reimer… - Clinical …, 2021 - Springer
Purpose Volumetric assessment of meningiomas represents a valuable tool for treatment
planning and evaluation of tumor growth as it enables a more precise assessment of tumor …

AdaptAhead optimization algorithm for learning deep CNN applied to MRI segmentation

F Hoseini, A Shahbahrami, P Bayat - Journal of digital imaging, 2019 - Springer
Deep learning is one of the subsets of machine learning that is widely used in artificial
intelligence (AI) field such as natural language processing and machine vision. The deep …

[HTML][HTML] Current applications of deep-learning in neuro-oncological MRI

CML Zegers, J Posch, A Traverso, D Eekers… - Physica Medica, 2021 - Elsevier
Abstract Purpose Magnetic Resonance Imaging (MRI) provides an essential contribution in
the screening, detection, diagnosis, staging, treatment and follow-up in patients with a …

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

L Zhou, Q Ji, H Peng, F Chen, Y Zheng, Z Jiao… - European …, 2024 - Springer
Objectives To develop a dynamic nomogram containing radiomics signature and clinical
features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and …