[HTML][HTML] RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields
Segmentation of brain lesions from magnetic resonance images (MRI) is an important step
for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to …
for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to …
Different medical data mining approaches based prediction of ischemic stroke
Aim Medical data mining (also called knowledge discovery process in medicine) processes
for extracting patterns from large datasets. In the current study, we intend to assess different …
for extracting patterns from large datasets. In the current study, we intend to assess different …
Ischemic stroke lesion segmentation using stacked sparse autoencoder
GB Praveen, A Agrawal, P Sundaram… - Computers in biology and …, 2018 - Elsevier
Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic
Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion …
Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion …
Ischemic lesion segmentation using ensemble of multi-scale region aligned CNN
The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion
from radiological images for effective treatment planning. Manual delineation of the lesion by …
from radiological images for effective treatment planning. Manual delineation of the lesion by …
Computer-aided detection and characterization of stroke lesion–a short review on the current state-of-the art methods
The fast advancements in the field of computer vision, progress in radiology, image
processing, modelling and simulation have changed the medical science to diagnose …
processing, modelling and simulation have changed the medical science to diagnose …
Deep random forest-based learning transfer to SVM for brain tumor segmentation
S Amiri, I Rekik, MA Mahjoub - 2016 2nd International …, 2016 - ieeexplore.ieee.org
Using neuroimaging techniques to diagnose brain tumors and detect both visible and
invisible cancer cells infiltration boundaries motivated the emergence of diverse tumor …
invisible cancer cells infiltration boundaries motivated the emergence of diverse tumor …
Segmentation of ischemic stroke lesion from 3d mr images using random forest
This paper focuses on segmentation of ischemic stroke lesion from the dataset contributed
by Ischemic Stroke Lesion Segmentation (ISLES)-2015 Sub-acute Ischemic Stroke lesion …
by Ischemic Stroke Lesion Segmentation (ISLES)-2015 Sub-acute Ischemic Stroke lesion …
Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images
PG Bharathi, A Agrawal, P Sundaram… - Biocybernetics and …, 2019 - Elsevier
Detection of ischemic stroke lesions plays a vital role in the assessment of stroke treatments
such as thrombolytic therapy and embolectomy. Manual detection and quantification of …
such as thrombolytic therapy and embolectomy. Manual detection and quantification of …
Res2U++: Deep learning model for segmentation of ischemic stroke lesions
N Jazzar, A Douik - Biomedical Signal Processing and Control, 2025 - Elsevier
Stroke is a critical global health issue characterized by the interruption of blood flow to the
brain, resulting in a depletion of oxygen and nutrients and causing severe damage or death …
brain, resulting in a depletion of oxygen and nutrients and causing severe damage or death …
Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics
M Ameksa, Z Elamrani Abou Elassad… - Computer Methods in …, 2024 - Taylor & Francis
Stroke, as a critical global health concern and the second leading cause of death, occurs
when blood flow to the brain is interrupted. Although machine learning has advanced in …
when blood flow to the brain is interrupted. Although machine learning has advanced in …