Dolph et al., 2017 - Google Patents
Deep learning of texture and structural features for multiclass Alzheimer's disease classificationDolph et al., 2017
View PDF- Document ID
- 2636207552907044648
- Author
- Dolph C
- Alam M
- Shboul Z
- Samad M
- Iftekharuddin K
- Publication year
- Publication venue
- 2017 International Joint Conference on Neural Networks (IJCNN)
External Links
Snippet
This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific …
- 206010001897 Alzheimer's disease 0 title abstract description 72
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
- 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
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dolph et al. | Deep learning of texture and structural features for multiclass Alzheimer's disease classification | |
| Buvaneswari et al. | Deep learning-based segmentation in classification of Alzheimer’s disease | |
| Nigri et al. | Explainable deep CNNs for MRI-based diagnosis of Alzheimer’s disease | |
| Liu et al. | Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis | |
| Khazaee et al. | Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory | |
| Liu et al. | Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis | |
| Liu et al. | Multi-hypergraph learning for incomplete multimodality data | |
| Mahanand et al. | Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network | |
| Yang et al. | Discrimination between Alzheimer′ s Disease and Mild Cognitive Impairment Using SOM and PSO‐SVM | |
| Fanijo et al. | Artificial intelligence-powered analysis of medical images for early detection of neurodegenerative diseases | |
| Savio et al. | Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI | |
| Basheera et al. | Deep learning based Alzheimer's disease early diagnosis using T2w segmented gray matter MRI | |
| KR102599756B1 (en) | Providing method of diagnostic information on alzheimer's disease using structural, diffusion, and functional neuroimaging data and the APOE genotype | |
| UYSAL et al. | Classifying early and late mild cognitive impairment stages of Alzheimer’s disease by analyzing different brain areas | |
| Çelebi et al. | Leveraging Deep Learning for Enhanced Detection of Alzheimer's Disease Through Morphometric Analysis of Brain Images. | |
| Shukla et al. | Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques | |
| Guo et al. | ADHD-200 classification based on social network method | |
| Taghavirashidizadeh et al. | WTD‐PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time‐Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease | |
| Hasan et al. | Detection of parkinson’s disease from t2-weighted magnetic resonance imaging scans using efficientnet-v2 | |
| Pahuja et al. | Early detection of Parkinson's disease through multimodal features using machine learning approaches | |
| Sabuncu | A Bayesian algorithm for image-based time-to-event prediction | |
| Ahmed et al. | Deepmrs: An end-to-end deep neural network for dementia disease detection using mrs data | |
| Brundha et al. | Skull stripping and classification of autism spectrum disorder using deep learning | |
| Yang et al. | Multi-dimensional proprio-proximus machine learning for assessment of myocardial infarction | |
| Kavitha | Study of tissue variation and analysis of MR brain images using optimized multilevel threshold and deep CNN features in neurodegenerative disorders |