☆ 4.6 Article

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods

JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Radiology, Nuclear Medicine & Medical Imaging

Usefulness of subtraction images for accurate diagnosis of pituitary microadenomas in dynamic contrast-enhanced magnetic resonance imaging

Morteza Sanei Taheri et al.

Summary: The study evaluated the usefulness of subtraction images in enhancing the visual detection of pituitary microadenomas. The results showed that subtraction images can successfully identify all lesions detectable with conventional dynamic MRI and improve visualization of lesions undetected on dynamic MRI, especially in small lesions.

ACTA RADIOLOGICA (2023)

添加到收藏夹

Article Computer Science, Hardware & Architecture

A new adaptive selection strategy for reducing latency in networks on chip

Mohammad Trik et al.

Summary: This paper proposes a hybrid method called ScRD for selecting a better output channel and improving NOC performance. By applying the ScRD algorithm and analyzing the traffic packets, it reduces packet delay and increases throughput by selecting the appropriate output channel. It also decreases energy consumption by distinguishing between local and non-local traffic.

INTEGRATION-THE VLSI JOURNAL (2023)

添加到收藏夹

Article Oncology

Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare

Moslem Samiei et al.

Summary: Through the use of big data and fuzzy AHP technique, early-stage skin cancer can be accurately predicted and diagnosed. This study combines support vector machine multi-class classification with fuzzy selector to classify and assess skin cancer. The results show that this approach maintains stability and achieves an accuracy rate of 90.86% in handling large datasets.

JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY (2023)

添加到收藏夹

Article Computer Science, Information Systems

Analysis of depth variation of U-NET architecture for brain tumor segmentation

Biswajit Jena et al.

Summary: This paper investigates the impact of depth variation on the performance of the U-Net architecture for biomedical image segmentation. The analysis using the BraTS-2017 and BraTS-2019 datasets shows that reducing the depth of the U-Net model does not significantly affect performance and can decrease computational complexity.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

添加到收藏夹

Article Automation & Control Systems

Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework

Yanwei Zhao et al.

Summary: This paper addresses the adaptive optimal containment control issue for non-affine nonlinear multi-agent systems in the presence of periodic disturbances. A Fourier series expansion-neural networks-based adaptive identifier is designed for each follower to deal with the disturbed internal dynamics, releasing the restrictions on the system dynamics. An adaptive dynamic programming technique is adopted to acquire the optimized virtual and actual controllers under a simplified actor-critic architecture. The simulation results demonstrate the applicability and effectiveness of the proposed containment control scheme.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework

Sanjay Saxena et al.

Summary: This study investigated the use of hybrid radiomics signatures derived from multi-parametric MRI to predict MGMT status and overall survival in GBM patients, providing guidance for treatment and surgical planning. The results showed that the application of conventional and deep radiomic features had good predictive capabilities for MGMT status and OS.

NEURAL COMPUTING & APPLICATIONS (2023)

添加到收藏夹

Article Multidisciplinary Sciences

SGHRP: Secure Greedy Highway Routing Protocol with authentication and increased privacy in vehicular ad hoc networks

Edris Khezri et al.

Summary: VANETs are networks of connected intelligent vehicles that can communicate with each other, as well as with infrastructure and fixed roadside equipment. A secure routing protocol called Secure Greedy Highway Routing Protocol (GHRP) has been proposed, which ensures confidentiality by using a chain of source keys validated by a one-way function. The protocol performs better than the SAODV protocol in terms of packet delivery rate, overhead, and average end-to-end delay, as confirmed by simulations using the NS2 simulator.

PLOS ONE (2023)

添加到收藏夹

Article Computer Science, Information Systems

Low-Computation Adaptive Saturated Self-Triggered Tracking Control of Uncertain Networked Systems

Wenjing Wu et al.

Summary: In this paper, a low-computation adaptive self-triggered tracking control scheme is proposed for a class of strict-feedback nonlinear systems with input saturation. Two novel error transformation functions are introduced to address the complexity explosion problem without filters, making the control scheme more practical. A self-triggered communication strategy is proposed to save communication resources in networked systems by predicting the next trigger point based on current information. The effectiveness of the developed control scheme is verified through a simulation example.

ELECTRONICS (2023)

添加到收藏夹

Article Biology

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation

Sanjay Saxena et al.

Summary: This study used multi-parametric MRI and machine learning methods to analyze the treatment plan for glioblastoma and developed a predictive model for MGMT methylation biomarkers.

COMPUTERS IN BIOLOGY AND MEDICINE (2023)

添加到收藏夹

Article Mathematics, Applied

Adaptive optimized consensus control for a class of nonlinear multi-agent systems with asymmetric input saturation constraints and hybrid faults

Fanghua Tang et al.

Summary: This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. With the aim of improving fault-tolerance and system performance, the authors use simplified smooth functions to approximate the asymmetric saturation model and design compensation signals for different types of faults. The effectiveness of the proposed control scheme is demonstrated through two simulation examples.

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION (2023)

添加到收藏夹

Article Automation & Control Systems

Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints

Sai Huang et al.

Summary: This paper proposes an adaptive fuzzy self-triggered tracking controller design method for full-state constrained multiple-input and multiple-output nonlinear systems. The implementation of the control scheme is divided into three steps: constraining the states to satisfy the corresponding constraints, addressing the explosion of complexity, and achieving a better compromise between system performances and communication loads. The proposed control approach effectively solves the problem of monitoring the triggering threshold continuously and allows for dynamically adjusting the triggering intervals according to the tracking errors.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2023)

添加到收藏夹

Article Automation & Control Systems

Fault Detection and Performance Recovery Design With Deferred Actuator Replacement via a Low-Computation Method

Fabin Cheng et al.

Summary: This paper proposes a low-computation design scheme for fault detection and performance recovery based on deferred replacement actuators for a class of uncertain nonlinear systems. The proposed method does not require prior knowledge of fault models, nor does it require multiple actuators working in parallel to mitigate the impact of faults. It achieves performance recovery by designing fault detection and shifting functions, and establishes a computationally efficient design scheme.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2023)

添加到收藏夹

Article Computer Science, Information Systems

A Novel Highway Routing Protocol in Vehicular Ad Hoc Networks Using VMaSC-LTE and DBA-MAC Protocols

Edris Khezri et al.

Summary: This paper introduces the vehicular ad hoc networks (VANETs) and the challenges they face, and proposes a highway routing protocol to address these issues. Through simulation experiments, the results show that the proposed protocol outperforms other protocols in terms of delay, packet delivery rate, and routing overhead.

WIRELESS COMMUNICATIONS & MOBILE COMPUTING (2022)

添加到收藏夹

Review Oncology

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

Thomas C. Booth et al.

Summary: This study systematically reviewed the quality and performance accuracy of recently published studies on using machine learning to monitor biomarkers for determining glioblastoma treatment response. The results showed that ML models using MRI features to distinguish between progression and mimics demonstrated good diagnostic performance. However, the limited number of studies, high bias risk, and applicability concerns in these studies call for improvement in study quality and design.

FRONTIERS IN ONCOLOGY (2022)

添加到收藏夹

Article Biochemical Research Methods

Distance-based Support Vector Machine to Predict DNA N6-methyladenine Modification

Haoyu Zhang et al.

Summary: This study presents a novel model based on sequence distance matrix and support vector machine (SVM) for predicting DNA 6mA modification. The model achieved high accuracy rates and correlation coefficients on rice and mouse data, showing significant advantages over traditional machine learning methods.

CURRENT BIOINFORMATICS (2022)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis

Abhishta Bhandari et al.

Summary: Machine learning models can effectively differentiate treatment-related effects and tumor progression through imaging. However, there are still challenges regarding article quality and integration into clinical practice.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2022)

添加到收藏夹

Article Multidisciplinary Sciences

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Anahita Fathi Kazerooni et al.

Summary: The study demonstrates the value of integrated diagnostics, using both conventional and deep learning methods, in predicting overall survival in IDH-wildtype GBM patients. Multi-omic data capture multi-faceted tumor characteristics and contribute to a comprehensive risk assessment of patients.

SCIENTIFIC REPORTS (2022)

添加到收藏夹

Article Engineering, Electrical & Electronic

A Hybrid Selection Strategy Based on Traffic Analysis for Improving Performance in Networks on Chip

Mohammad Trik et al.

Summary: Networks on chip (NoCs) are a concept for implementing multiprocessor systems that handle communication between processing cores. A new hybrid algorithm called ScRN is introduced in this study to improve NoC performance by selecting better output channels based on traffic characteristics and system status.

JOURNAL OF SENSORS (2022)

添加到收藏夹

Article Pharmacology & Pharmacy

Gli1 promotes epithelial-mesenchymal transition and metastasis of non-small cell lung carcinoma by regulating snail transcriptional activity and stability

Xueping Lei et al.

Summary: Gli1 promotes the metastasis of non-small cell lung carcinoma and is associated with poor patient survival.

ACTA PHARMACEUTICA SINICA B (2022)

添加到收藏夹

Article Computer Science, Cybernetics

PBPHS: A Profile-Based Predictive Handover Strategy for 5G Networks

Jiabao Sun et al.

Summary: One of the necessities of mobile networks is uninterrupted access to wireless services. With the development of 5G, improvements will be achieved in several aspects such as seamless communications and energy efficiency. This paper presents a profile-based predictive handover strategy to select the best alternative for handover and reduce unnecessary handovers. The simulation results show that the proposed strategy outperforms state-of-the-art methods in handover reduction rate.

CYBERNETICS AND SYSTEMS (2022)

添加到收藏夹

Review Oncology

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis

Omar Kouli et al.

Summary: This study evaluated the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation. The results showed that DL outperformed TML in terms of false positive rate and tumor core segmentation. Although automated segmentation had similar performance to manual segmentation for clinical practice, manual methods were superior for sub-compartmental segmentation.

NEURO-ONCOLOGY ADVANCES (2022)

添加到收藏夹

Article Computer Science, Information Systems

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

Andres Anaya-Isaza et al.

Summary: The exponential growth of deep learning networks has enabled the handling of complex tasks in fields like medicine. Data augmentation methods play a vital role in training networks with limited data access. By comparing different data augmentation techniques and incorporating principal component analysis, the research achieved efficient brain tumor detection using the ResNet50 network.

IEEE ACCESS (2022)

添加到收藏夹

Article Clinical Neurology

Brain Glioma Localization Diagnosis Based on Magnetic Resonance Imaging

Yong Jin et al.

Summary: This study utilized DCE-MRI technology and its perfusion parameters to diagnose and differentially diagnose brain tumors, showing that DCE-MRI can accurately distinguish between HGG and LGG, with t-Ktrans value demonstrating higher specificity and sensitivity.

WORLD NEUROSURGERY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Brain tumor classification in magnetic resonance image using hard swish-based RELU activation function-convolutional neural network

Afnan M. Alhassan et al.

Summary: Automatic classification of brain tumor types using a deep learning model, which extracts feature vectors from brain images through normalization and histogram techniques, and inputs them into convolutional neural networks for classification. The proposed model achieved an accuracy of 98.6% in experiments, outperforming existing algorithms in the field.

NEURAL COMPUTING & APPLICATIONS (2021)

添加到收藏夹

Article Clinical Neurology

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario

Antonio Di Ieva et al.

Summary: The study evaluated the accuracy of brain tumor segmentation using a deep learning model, finding that the model performed best when trained with specific MRI sequences and that FLAIR and T1C sequences were important contributors to performance. The model not only accurately segmented gliomas but also non-glioma cases in the local dataset.

NEURORADIOLOGY (2021)

添加到收藏夹

Review Clinical Neurology

Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Anne Jian et al.

Summary: Machine learning models show good performance in predicting molecular subtyping of gliomas, especially for predicting IDH mutation, MGMT methylation, and 1p/19q codeletion with high accuracy. Data augmentation and MRI sequence type are weakly associated with heterogeneity.

NEUROSURGERY (2021)

添加到收藏夹

Article Oncology

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

Satoshi Takahashi et al.

Summary: The study proposed a method to address the issue of image differences among facilities causing performance degradation in automated magnetic resonance image segmentation for glioma detection. The results showed significant differences in the performance of the models developed using different datasets, with the fine-tuning model achieving the highest Dice coefficient score.

CANCERS (2021)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Evi J. van Kempen et al.

Summary: The study provides an overview and meta-analysis of various machine learning algorithms for automated segmentation of gliomas, showing promising accuracy. However, high heterogeneity between studies and publication bias were observed as potential challenges.

EUROPEAN RADIOLOGY (2021)

添加到收藏夹

Article Mathematical & Computational Biology

Providing an Adaptive Routing along with a Hybrid Selection Strategy to Increase Efficiency in NoC-Based Neuromorphic Systems

Mohammad Trik et al.

Summary: The study introduces an adaptive routing algorithm and hybrid selection strategy for NoC-based neuromorphic systems, demonstrating through experiments a significant reduction in average delay time and power consumption.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

添加到收藏夹

Review Oncology

Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Evi J. van Kempen et al.

Summary: Current research shows promising results in using machine learning algorithms to determine molecular characteristics of gliomas non-invasively, with a focus on IDH1/2 genotype. The accuracy for classifying 1p/19q codeletion status is relatively lower. Despite encouraging findings, large-scale prospective trials and external validation are necessary for future implementation.

CANCERS (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

A Deep Transfer Learning Approach for Automated Detection of Brain Tumor Through Magnetic Resonance Imaging

Sahar Gull et al.

Summary: This study introduces a unique segmentation and classification framework for brain tumors based on transfer learning, achieving high accuracy in tumor classification with pre-trained convolutional neural networks.

4TH INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING (IC)2 (2021)

添加到收藏夹

Article Pharmacology & Pharmacy

Lineage reprogramming of fibroblasts into induced cardiac progenitor cells by CRISPR/Cas9-based transcriptional activators

Jianglin Wang et al.

ACTA PHARMACEUTICA SINICA B (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

Hiba Mzoughi et al.

JOURNAL OF DIGITAL IMAGING (2020)

添加到收藏夹

Article Engineering, Biomedical

DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images

Ramy A. Zeineldin et al.

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2020)

添加到收藏夹

Article Engineering, Biomedical

Homotypic Targeting Delivery of siRNA with Artificial Cancer Cells

Lingmin Zhang et al.

ADVANCED HEALTHCARE MATERIALS (2020)

添加到收藏夹

Article Engineering, Biomedical

A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

Tiejun Yang et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2019)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

Mahmoud Khaled Abd-Ellah et al.

MAGNETIC RESONANCE IMAGING (2019)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

State of the Art: Machine Learning Applications in Glioma Imaging

Eyal Lotan et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2019)

添加到收藏夹

Article Health Care Sciences & Services

Blood Sugar Level Indication Through Chewing and Swallowing from Acoustic MEMS Sensor and Deep Learning Algorithm for Diabetic Management

S. Krishna Kumari et al.

JOURNAL OF MEDICAL SYSTEMS (2019)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Deep learning in medical imaging and radiation therapy

Berkman Sahiner et al.

MEDICAL PHYSICS (2019)

添加到收藏夹

Review Oncology

A Review on a Deep Learning Perspective in Brain Cancer Classification

Gopal S. Tandel et al.

CANCERS (2019)

添加到收藏夹

Article Neurosciences

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

Yang Yang et al.

FRONTIERS IN NEUROSCIENCE (2018)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Deep Learning in Medical Imaging: General Overview

June-Goo Lee et al.

KOREAN JOURNAL OF RADIOLOGY (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Brain tumor segmentation with Deep Neural Networks

Mohammad Havaei et al.

MEDICAL IMAGE ANALYSIS (2017)

添加到收藏夹

Review Clinical Neurology

The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary

David N. Louis et al.

ACTA NEUROPATHOLOGICA (2016)

添加到收藏夹

Article Clinical Neurology

Low-grade Glioma Surgery in Intraoperative Magnetic Resonance Imaging: Results of a Multicenter Retrospective Assessment of the German Study Group for Intraoperative Magnetic Resonance Imaging

Jan Coburger et al.

NEUROSURGERY (2016)

添加到收藏夹

Article Oncology

Exciting New Advances in Neuro-Oncology The Avenue to a Cure for Malignant Glioma

Erwin G. Van Meir et al.

CA-A CANCER JOURNAL FOR CLINICIANS (2010)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.