Nothing Special   »   [go: up one dir, main page]

Agarwal et al., 2021 - Google Patents

Transfer learning for Alzheimer's disease through neuroimaging biomarkers: a systematic review

Agarwal et al., 2021

View HTML
Document ID
3894427683272425448
Author
Agarwal D
Marques G
de la Torre-Díez I
Franco Martin M
García Zapiraín B
Martín Rodríguez F
Publication year
Publication venue
Sensors

External Links

Snippet

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
Agarwal et al. Transfer learning for Alzheimer’s disease through neuroimaging biomarkers: a systematic review
Odusami et al. Analysis of features of Alzheimer’s disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network
Yamanakkanavar et al. MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey
Xie et al. Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): Review, challenges, and future perspectives
Chakraborty et al. Detection of Parkinson’s disease from 3T T1 weighted MRI scans using 3D convolutional neural network
Saratxaga et al. MRI deep learning-based solution for Alzheimer’s disease prediction
Maqsood et al. Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans
Rehman et al. Conventional and deep learning methods for skull stripping in brain MRI
Shukla et al. Review on alzheimer disease detection methods: Automatic pipelines and machine learning techniques
Mori et al. Atlas-based neuroinformatics via MRI: harnessing information from past clinical cases and quantitative image analysis for patient care
Castillo et al. MR images, brain lesions, and deep learning
Cui et al. Artificial intelligence in spinal imaging: current status and future directions
Atia et al. Particle swarm optimization and two-way fixed-effects analysis of variance for efficient brain tumor segmentation
Caponetti et al. MR brain image segmentation: a framework to compare different clustering techniques
Qian et al. Recent advances in explainable artificial intelligence for magnetic resonance imaging
Amerikanos et al. Image analysis in digital pathology utilizing machine learning and deep neural networks
Khalid et al. Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features
Saladi et al. Segmentation and analysis emphasizing neonatal MRI brain images using machine learning techniques
Latif DeepTumor: framework for Brain MR image classification, Segmentation and Tumor Detection
Alksas et al. A novel system for precise grading of glioma
Ismail et al. Multforad: Multimodal mri neuroimaging for alzheimer’s disease detection based on a 3d convolution model
Andrushia et al. Visual-saliency-based abnormality detection for MRI brain images—Alzheimer’s disease analysis
Lo et al. Intelligent glioma grading based on deep transfer learning of MRI radiomic features
Al-Mekhlafi et al. Diagnosis of histopathological images to distinguish types of malignant lymphomas using hybrid techniques based on fusion features
Tuvshinjargal et al. VGG-C transform model with batch normalization to predict Alzheimer’s disease through MRI dataset