Bashir-Gonbadi et al., 2021 - Google Patents
Brain tumor classification using deep convolutional autoencoder-based neural network: Multi-task approachBashir-Gonbadi et al., 2021
- Document ID
- 1762702791766422008
- Author
- Bashir-Gonbadi F
- Khotanlou H
- Publication year
- Publication venue
- Multimedia tools and applications
External Links
Snippet
Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional …
- 208000003174 Brain Neoplasms 0 title abstract description 41
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
- 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
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
- 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
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hirra et al. | Breast cancer classification from histopathological images using patch-based deep learning modeling | |
Gupta et al. | Brain tumor detection and classification using cycle generative adversarial networks | |
Bashir-Gonbadi et al. | Brain tumor classification using deep convolutional autoencoder-based neural network: Multi-task approach | |
Neelima et al. | Optimal DeepMRSeg based tumor segmentation with GAN for brain tumor classification | |
Demir et al. | Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data | |
Zuo et al. | Multimodal representations learning and adversarial hypergraph fusion for early Alzheimer’s disease prediction | |
Wang et al. | Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease | |
Deepa et al. | Hybrid optimization algorithm enabled deep learning approach brain tumor segmentation and classification using MRI | |
Khan et al. | Segmentation of shoulder muscle MRI using a new region and edge based deep auto-encoder | |
Bakkouri et al. | BG-3DM2F: bidirectional gated 3D multi-scale feature fusion for Alzheimer’s disease diagnosis | |
Mishra et al. | Graph attention autoencoder inspired CNN based brain tumor classification using MRI | |
Oza et al. | Deep ensemble transfer learning-based framework for mammographic image classification | |
Bansal et al. | An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques | |
Zheng et al. | Small lesion classification in dynamic contrast enhancement MRI for breast cancer early detection | |
Jyotiyana et al. | Deep learning and the future of biomedical image analysis | |
Murmu et al. | A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor | |
Dhiravidachelvi et al. | Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm | |
Karacı et al. | YoDenBi-NET: YOLO+ DenseNet+ Bi-LSTM-based hybrid deep learning model for brain tumor classification | |
Li et al. | A multi-label classification model for full slice brain computerised tomography image | |
Patil et al. | A new automated segmentation and classification of mammogram images | |
Aruna Devi et al. | Performance evaluation of MRI pancreas image classification using artificial neural network (ANN) | |
Devanathan et al. | Multi-objective Archimedes Optimization Algorithm with Fusion-based Deep Learning model for brain tumor diagnosis and classification | |
Sharma et al. | DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images | |
Khan et al. | Deep-Net: Fine-tuned deep neural network multi-features fusion for brain tumor recognition | |
Khalifa et al. | Deep learning for image segmentation: a focus on medical imaging |