Yu et al., 2020 - Google Patents
Convolutional neural network design for breast cancer medical image classificationYu et al., 2020
View PDF- Document ID
- 9257386450447660256
- Author
- Yu Y
- Favour E
- Mazumder P
- Publication year
- Publication venue
- 2020 IEEE 20th International Conference on Communication Technology (ICCT)
External Links
Snippet
For computer-aided diagnosis of medical issues, image classification plays an important role to ensure high prediction accuracy. Convolutional neural network (CNN) is an important aspect of deep learning because it returns a higher performance rate than other traditional …
- 230000001537 neural 0 title abstract description 29
Classifications
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
-
- 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
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- 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
- G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
- G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
-
- 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
- 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
- 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
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- 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 |
---|---|---|
Sahiner et al. | Deep learning in medical imaging and radiation therapy | |
Cai et al. | A review of the application of deep learning in medical image classification and segmentation | |
Yousef et al. | A holistic overview of deep learning approach in medical imaging | |
Radak et al. | Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies | |
Liu et al. | A semi-supervised convolutional transfer neural network for 3D pulmonary nodules detection | |
Santos-Bustos et al. | Towards automated eye cancer classification via VGG and ResNet networks using transfer learning | |
Xu et al. | Pulmonary textures classification via a multi-scale attention network | |
Yu et al. | Convolutional neural network design for breast cancer medical image classification | |
Mridha et al. | A comprehensive survey on the progress, process, and challenges of lung cancer detection and classification | |
Tian et al. | Radiomics and its clinical application: artificial intelligence and medical big data | |
Li et al. | Medical image identification methods: a review | |
Murmu et al. | Deep learning model-based segmentation of medical diseases from MRI and CT images | |
Hassan et al. | Image classification based deep learning: A Review | |
Katran et al. | Deep Learning's Impact on MRI Image Analysis: A Comprehensive Survey | |
Liu et al. | Breast cancer classification method based on improved VGG16 using mammography images | |
Román et al. | Hyperparameter Tuning in a Dual Channel U-Net for Medical Image Segmentation | |
Srivastava et al. | Optimizing CNN based model for thyroid nodule classification using data augmentation, segmentation and boundary detection techniques | |
Abdulwahhab et al. | A review on medical image applications based on deep learning techniques | |
Simie et al. | Lung cancer detection using convolutional neural network (CNN) | |
Haq | An overview of deep learning in medical imaging | |
Keerthi et al. | A Review on Brain Tumor Prediction using Deep Learning | |
Kumari et al. | Role of Computed Tomography Imaging for the Diagnosis and Classification of Lung Cancer using Machine Learning | |
Godishala et al. | Breast cancer tumor image classification using deep learning image data generator | |
Sathishkumar et al. | Detection and Classification of Breast cancer from Ultrasound Images using NASNet Model | |
Ghani | On forecasting lung cancer patients’ survival rates using 3D feature engineering |