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 of medical image segmentation algorithms

KKD Ramesh, GK Kumar, K Swapna… - … on Pervasive Health …, 2021 - publications.eai.eu
INTRODUCTION: Image segmentation in medical physics plays a vital role in image
analysis to identify the affected tumour. The process of subdividing an image into its …

A survey on brain tumor detection techniques for MR images

PK Chahal, S Pandey, S Goel - Multimedia Tools and Applications, 2020 - Springer
One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

P Naga Srinivasu, TB Krishna, S Ahmed… - Journal of …, 2023 - Wiley Online Library
Over the past few years, a tremendous change has occurred in computer‐aided diagnosis
(CAD) technology. The evolution of numerous medical imaging techniques has enhanced …

A hybrid weighted fuzzy approach for brain tumor segmentation using MR images

PK Chahal, S Pandey - Neural Computing and Applications, 2023 - Springer
Human brain tumor detection and classification are time-consuming however vital tasks for
any medical expert. Assistance via computer aided diagnosis is commonly used to enhance …

[PDF][PDF] Automated brain tumor classification using various deep learning models: a comparative study

AA Abbood, QM Shallal… - … Journal of Electrical …, 2021 - pdfs.semanticscholar.org
The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan.
Thus, planning treatments is a crucial step in improving a patient's quality of life. In general …

Application of machine learning techniques for characterization of ischemic stroke with MRI images: a review

A Subudhi, P Dash, M Mohapatra, RS Tan, UR Acharya… - Diagnostics, 2022 - mdpi.com
Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …

Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm

CC Benson, V Deepa, VL Lajish… - … on advances in …, 2016 - ieeexplore.ieee.org
Brain is the master and commanding organ of human body. Human brain is affected by
many dangerous diseases. Brain tumor or neoplasm is the abnormal growth of tissues in the …

A Deep‐Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

H Liu, Q Li, IC Wang - Mathematical Problems in Engineering, 2021 - Wiley Online Library
The segmentation of brain tumors in medical images is a crucial step of clinical treatment.
Manual segmentation is time consuming and labor intensive, and existing automatic …

Autopaint: A self-inpainting method for unsupervised anomaly detection

M Astaraki, F De Benetti, Y Yeganeh… - arXiv preprint arXiv …, 2023 - arxiv.org
Robust and accurate detection and segmentation of heterogenous tumors appearing in
different anatomical organs with supervised methods require large-scale labeled datasets …