Image segmentation for MR brain tumor detection using machine learning: a review

TA Soomro, L Zheng, AJ Afifi, A Ali… - IEEE Reviews in …, 2022 - ieeexplore.ieee.org
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

AS Neethi, SK Kannath, AA Kumar, J Mathew… - … Applications of Artificial …, 2024 - Elsevier
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …

Intracranial hemorrhage segmentation using a deep convolutional model

MD Hssayeni, MS Croock, AD Salman, HF Al-Khafaji… - Data, 2020 - mdpi.com
Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …

Optimized brain tumor detection: a dual-module approach for mri image enhancement and tumor classification

AA Asiri, TA Soomro, AA Shah, G Pogrebna… - IEEE …, 2024 - ieeexplore.ieee.org
Neurological and brain-related cancers are one of the main causes of death worldwide. A
commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI) …

Deep learning for hemorrhagic lesion detection and segmentation on brain CT images

L Li, M Wei, BO Liu, K Atchaneeyasakul… - IEEE journal of …, 2020 - ieeexplore.ieee.org
Stroke is an acute cerebral vascular disease that is likely to cause long-term disabilities and
death. Immediate emergency care with accurate diagnosis of computed tomographic (CT) …

Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks

J Ye, J Cheng, J Chen, Z Deng, T Li, H Wang… - arxiv preprint arxiv …, 2023 - arxiv.org
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …

Improved brain tumor segmentation and classification in brain MRI with FCM-SVM: a diagnostic approach

SM Alqhtani, TA Soomro, A Ali, A Aziz, M Irfan… - IEEE …, 2024 - ieeexplore.ieee.org
Cancer associated with the nervous system and brain tumors ranks among the leading
causes of death in various countries. Magnetic resonance imaging (MRI) and computed …

Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai

P Chen, J Ye, G Wang, Y Li, Z Deng, W Li, T Li… - arxiv preprint arxiv …, 2024 - arxiv.org
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as
imaging, text, and physiological signals, and can be applied in various fields. In the medical …

Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks.

A Tursynova, B Omarov, A Sakhipov… - … Journal of Online & …, 2022 - search.ebscohost.com
Segmentation of brain regions affected by ischemic stroke helps to overcome the main
obstacles in modern studies of stroke visualization. Unfortunately, contemporary methods of …

Bhsd: A 3d multi-class brain hemorrhage segmentation dataset

B Wu, Y **e, Z Zhang, J Ge, K Yaxley, S Bahadir… - … Workshop on Machine …, 2023 - Springer
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside
the skull or brain, which can be attributed to various factors. Identifying, localizing and …