Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019

A Tiwari, S Srivastava, M Pant - Pattern recognition letters, 2020 - Elsevier
The past few years have witnessed a significant increase in medical cases related to brain
tumors, making it the 10th most common form of tumor affecting children and adults alike …

A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image

S Ali, J Li, Y Pei, R Khurram, KU Rehman… - … methods in engineering, 2022 - Springer
The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …

A survey of brain tumor segmentation and classification algorithms

ES Biratu, F Schwenker, YM Ayano, TG Debelee - Journal of Imaging, 2021 - mdpi.com
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several
slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from …

Design and implementing brain tumor detection using machine learning approach

G Hemanth, M Janardhan… - 2019 3rd international …, 2019 - ieeexplore.ieee.org
Nowadays, brain tumor detection has turned upas a general causality in the realm of health
care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply …

[图书][B] Computer Vision and Recognition Systems: Research Innovations and Trends

CL Chowdhary, GT Reddy, BD Parameshachari - 2022 - api.taylorfrancis.com
This cutting-edge volume, Computer Vision and Recognition Systems: Research
Innovations and Trends, focuses on how artificial intelligence can be used to give computers …

Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images

P Khodaee, A Esfahanipour, HM Taheri - Engineering Applications of …, 2022 - Elsevier
This paper aims to forecast stock price Turning Points (TPs) with a developed hybrid
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. To this …

Automated brain tumour segmentation techniques—a review

M Angulakshmi… - International Journal of …, 2017 - Wiley Online Library
Automatic segmentation of brain tumour is the process of separating abnormal tissues from
normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) …

Efficient U-Net architecture with multiple encoders and attention mechanism decoders for brain tumor segmentation

I Aboussaleh, J Riffi, KE Fazazy, MA Mahraz, H Tairi - Diagnostics, 2023 - mdpi.com
The brain is the center of human control and communication. Hence, it is very important to
protect it and provide ideal conditions for it to function. Brain cancer remains one of the …

[PDF][PDF] Hybrid manta ray foraging optimization for novel brain tumor detection

P Karuppusamy - Journal of Soft Computing Paradigm (JSCP), 2020 - scholar.archive.org
In medical image processing, segmentation and extraction of tumor portion from brain MRI is
a complex task. It consumes more time and human effort to differentiate the normal and …

FDCNet: Presentation of the fuzzy CNN and fractal feature extraction for detection and classification of tumors

S Molaei, N Ghorbani, F Dashtiahangar… - Computational …, 2022 - Wiley Online Library
The detection of brain tumors using magnetic resonance imaging is currently one of the
biggest challenges in artificial intelligence and medical engineering. It is important to identify …