[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Soewu et al., 2022 - Google Patents

Convolutional Neural Networks for MRI-Based Brain Tumor Classification

Soewu et al., 2022

Document ID
15237608639907463325
Author
Soewu T
Singh D
Rakhra M
Chakraborty G
Singh A
Publication year
Publication venue
2022 3rd International Conference on Computation, Automation and Knowledge Management (ICCAKM)

External Links

Snippet

Recently, the rate at which people die from cancer disease is very alarming. The major reason is because the cancer cells were not detected at the early stages. It may be difficult for medical professionals to evaluate the results of medical imaging techniques such as …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
Soewu et al. Convolutional Neural Networks for MRI-Based Brain Tumor Classification
Kamran et al. RV-GAN: Segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network
Wong et al. Deep learning-based cardiovascular image diagnosis: a promising challenge
Altaf et al. Going deep in medical image analysis: concepts, methods, challenges, and future directions
Parekh et al. Deep learning and radiomics in precision medicine
Moitra et al. Classification of non-small cell lung cancer using one-dimensional convolutional neural network
Meng et al. Regression of instance boundary by aggregated CNN and GCN
Varma et al. SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning
Ravishankar et al. Joint deep learning of foreground, background and shape for robust contextual segmentation
Hassan et al. Image classification based deep learning: A Review
Meer et al. Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images
Jayakumar et al. SADIR: shape-aware diffusion models for 3D image reconstruction
Ossenberg-Engels et al. Conditional generative adversarial networks for the prediction of cardiac contraction from individual frames
Vallathan et al. An analysis and study of brain cancer with RNN algorithm based AI technique
Jahangeer et al. Detecting breast cancer using novel mask R‐CNN techniques
Rath et al. A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation
Adebisi et al. Computer techniques for medical image classification: a review
Kumar et al. Segmentation of lung field in HRCT images using U-net based fully convolutional networks
Das et al. Particle Swarm Optimizer Variants for Multi-level Thresholding: Theory, Performance Enhancement and Evaluation
Dharani et al. Industry 4.0 transformation: adaptive coati deep convolutional neural network-based oral cancer diagnosis in histopathological images for clinical applications
Kapdi et al. Brain Tumor Segmentation Using Fully Convolution Neural Network
Singh et al. A Comparative Study of DL and ML Models for Brain Tumor Detection
Abirami et al. Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images
Ezhilarasan et al. Bio-Inspired Algorithms Used in Medical Image Processing
DJ et al. Liver tumor segmentation using G-Unet and the impact of preprocessing and postprocessing methods