An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
[HTML][HTML] Vision transformers in multi-modal brain tumor MRI segmentation: A review
Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …
which bring enormous challenges for the timely diagnosis and effective treatment of brain …
GMetaNet: Multi-scale ghost convolutional neural network with auxiliary MetaFormer decoding path for brain tumor segmentation
Y Lu, Y Chang, Z Zheng, Y Sun, M Zhao, B Yu… - … Signal Processing and …, 2023 - Elsevier
Automatic segmentation of brain tumors from multimodal MR images plays an important role
in treatment decision and operation planning. Thus, we propose a novel 3D multi-scale …
in treatment decision and operation planning. Thus, we propose a novel 3D multi-scale …
A systematic collection of medical image datasets for deep learning
The astounding success made by artificial intelligence in healthcare and other fields proves
that it can achieve human-like performance. However, success always comes with …
that it can achieve human-like performance. However, success always comes with …
A literature survey of MR-based brain tumor segmentation with missing modalities
Multimodal MR brain tumor segmentation is one of the hottest issues in the community of
medical image processing. However, acquiring the complete set of MR modalities is not …
medical image processing. However, acquiring the complete set of MR modalities is not …
Interpretable machine learning model to predict survival days of malignant brain tumor patients
An artificial intelligence (AI) model's performance is strongly influenced by the input features.
Therefore, it is vital to find the optimal feature set. It is more crucial for the survival prediction …
Therefore, it is vital to find the optimal feature set. It is more crucial for the survival prediction …
Feature-enhanced fusion of U-NET-based improved brain tumor images segmentation
AH Nizamani, Z Chen, AA Nizamani… - Journal of Cloud …, 2023 - Springer
The field of medical image segmentation, particularly in the context of brain tumor
delineation, plays an instrumental role in aiding healthcare professionals with diagnosis and …
delineation, plays an instrumental role in aiding healthcare professionals with diagnosis and …
State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions
Objective Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life
expectancy. Physicians and oncologists urgently require automated techniques in clinics for …
expectancy. Physicians and oncologists urgently require automated techniques in clinics for …
Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM
Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if
left untreated. While significant efforts have been made with some promising results, the …
left untreated. While significant efforts have been made with some promising results, the …
An efficient transfer learning-based model for classification of brain tumor
Brain tumor is ranked 12 th deadliest cancerous tumor among children and adults. An early
detection and identification of its type can help radiologists and medical practitioners in …
detection and identification of its type can help radiologists and medical practitioners in …