Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis
O Kouli, A Hassane, D Badran, T Kouli… - Neuro-oncology …, 2022 - academic.oup.com
Background Automated brain tumor identification facilitates diagnosis and treatment
planning. We evaluate the performance of traditional machine learning (TML) and deep …
planning. We evaluate the performance of traditional machine learning (TML) and deep …
Threshold prediction for segmenting tumour from brain MRI scans
MM Beno, V I. R, S S. M… - International Journal of …, 2014 - Wiley Online Library
In recent decades, region growing methods in image segmentation plays a vital role in
medical image processing. Nonetheless, the method needs more advancement to cope up …
medical image processing. Nonetheless, the method needs more advancement to cope up …
Image completion based on segmentation using neutrosophic sets
Image completion aims to restore the corrupted regions in an image. One of the most
important challenges in image completion is to find the most appropriate data for replacing …
important challenges in image completion is to find the most appropriate data for replacing …
[HTML][HTML] An efficient method for brain tumor detection using texture features and SVM classifier in MR images
M Devi, S Maheswaran - Asian Pacific journal of cancer …, 2018 - ncbi.nlm.nih.gov
Objective: Detection and classification of abnormalities in Magnetic Resonance (MR) brain
images in medical field is very much needed. The proposed brain tumor classification …
images in medical field is very much needed. The proposed brain tumor classification …
Ensemble classification of colon biopsy images based on information rich hybrid features
In recent years, classification of colon biopsy images has become an active research area.
Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process …
Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process …
Mixture model segmentation system for parasagittal meningioma brain tumor classification based on hybrid feature vector
Meningioma is the one of the most common type of brain tumor, it as arises from the
meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all …
meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all …
[PDF][PDF] Robust classification of brain tumor in MRI images using salient structure descriptor and RBF kernel-SVM
The most common type of brain tumor known as Meningioma arises from the meninges and
encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain …
encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain …
Abnormality segmentation and classification of multi-class brain tumor in MR images using fuzzy logic-based hybrid kernel SVM
Image classification is one of the typical computational applications widely used in the
medical field, especially for abnormality detection in magnetic resonance (MR) brain …
medical field, especially for abnormality detection in magnetic resonance (MR) brain …
Multi class brain tumor classification of MRI images using hybrid structure descriptor and fuzzy logic based RBF kernel SVM
Medical Image segmentation is to partition the image into a set of regions that are visually
obvious and consistent with respect to some properties such as gray level, texture or color …
obvious and consistent with respect to some properties such as gray level, texture or color …
Severity analysis of brain tumor in MRI images using modified multi-texton structure descriptor and kernel-SVM
Image segmentation is to recognize structures in the image that are expected to signify
scene objects. It is widely used by the radiologists to segment the medical images into …
scene objects. It is widely used by the radiologists to segment the medical images into …