Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): Review, challenges, and future perspectives
Y Xie, F Zaccagna, L Rundo, C Testa, R Agati, R Lodi… - Diagnostics, 2022 - mdpi.com
Convolutional neural networks (CNNs) constitute a widely used deep learning approach that
has frequently been applied to the problem of brain tumor diagnosis. Such techniques still …
has frequently been applied to the problem of brain tumor diagnosis. Such techniques still …
Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
Azo Reductase Activated Magnetic Resonance Tuning Probe with “Switch-On” Property for Specific and Sensitive Tumor Imaging in Vivo
P Gu, Y Li, L Li, S Deng, X Zhu, Y Song, E Song… - ACS …, 2023 - ACS Publications
Cancer remains a threat to human health. However, if tumors can be detected in the early
stage, then the effectiveness of cancer treatment could be significantly improved. Therefore …
stage, then the effectiveness of cancer treatment could be significantly improved. Therefore …
Least square-support vector machine based brain tumor classification system with multi model texture features
Radiologists confront formidable challenges when confronted with the intricate task of
classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript …
classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript …
Predicting survival in patients with brain tumors: Current state-of-the-art of AI methods applied to MRI
Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have
increasingly been used to define the best approaches for survival assessment and …
increasingly been used to define the best approaches for survival assessment and …
Enhancing Brain Tumor Diagnosis: Transitioning From Convolutional Neural Network to Involutional Neural Network
] Accurate classification of brain tumors is essential for effective medical diagnosis and
treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) …
treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) …
[HTML][HTML] Targeting Tumor Hypoxia with Nanoparticle-Based Therapies: Challenges, Opportunities, and Clinical Implications
Hypoxia is a crucial factor in tumor biology, affecting various solid tumors to different extents.
Its influence spans both early and advanced stages of cancer, altering cellular functions and …
Its influence spans both early and advanced stages of cancer, altering cellular functions and …
Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning
DNG Kasap, NGN Mora, DA Blömer, BH Akkurt… - Biomedicines, 2024 - mdpi.com
Objectives: Regarding the 2021 World Health Organization (WHO) classification of central
nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of …
nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of …
Comparing [18F] FET PET and [18F] FDOPA PET for glioma recurrence diagnosis: a systematic review and meta-analysis
P Yu, Y Wang, F Su, Y Chen - Frontiers in Oncology, 2024 - frontiersin.org
Purpose The purpose of our meta-analysis and systematic review was to evaluate and
compare the diagnostic effectiveness of [18F] FET PET and [18F] FDOPA PET in detecting …
compare the diagnostic effectiveness of [18F] FET PET and [18F] FDOPA PET in detecting …
Brain Tumor Recognition Using Artificial Intelligence Neural-Networks (BRAIN): A Cost-Effective Clean-Energy Platform
Brain tumors necessitate swift detection and classification for optimal patient outcomes.
Deep learning has been extensively utilized to recognize complex tumor patterns in …
Deep learning has been extensively utilized to recognize complex tumor patterns in …