Brain tumor detection and classification using machine learning: a comprehensive survey

J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022 - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …

A review on brain tumor segmentation of MRI images

A Wadhwa, A Bhardwaj, VS Verma - Magnetic resonance imaging, 2019 - Elsevier
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 …

Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI

Z Zhu, X He, G Qi, Y Li, B Cong, Y Liu - Information Fusion, 2023 - Elsevier
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

Brain tumor detection using fusion of hand crafted and deep learning features

T Saba, AS Mohamed, M El-Affendi, J Amin… - Cognitive Systems …, 2020 - Elsevier
The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by
damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor …

Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks

H Dong, G Yang, F Liu, Y Mo, Y Guo - … , MIUA 2017, Edinburgh, UK, July 11 …, 2017 - Springer
A major challenge in brain tumor treatment planning and quantitative evaluation is
determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) …

Extending nn-UNet for brain tumor segmentation

HM Luu, SH Park - International MICCAI brainlesion workshop, 2021 - Springer
Brain tumor segmentation is essential for the diagnosis and prognosis of patients with
gliomas. The brain tumor segmentation challenge has provided an abundant and high …

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

MK Abd-Ellah, AI Awad, AAM Khalaf… - Magnetic resonance …, 2019 - Elsevier
The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …

The multimodal brain tumor image segmentation benchmark (BRATS)

BH Menze, A Jakab, S Bauer… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …

DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images

RA Zeineldin, ME Karar, J Coburger, CR Wirtz… - International journal of …, 2020 - Springer
Purpose Gliomas are the most common and aggressive type of brain tumors due to their
infiltrative nature and rapid progression. The process of distinguishing tumor boundaries …

Big data analysis for brain tumor detection: Deep convolutional neural networks

J Amin, M Sharif, M Yasmin, SL Fernandes - Future Generation Computer …, 2018 - Elsevier
Brain tumor detection is an active area of research in brain image processing. In this work, a
methodology is proposed to segment and classify the brain tumor using magnetic resonance …