Nambisan et al., 2023 - Google Patents
Improving automatic melanoma diagnosis using deep learning-based segmentation of irregular networksNambisan et al., 2023
View HTML- Document ID
- 9237592522095767954
- Author
- Nambisan A
- Maurya A
- Lama N
- Phan T
- Patel G
- Miller K
- Lama B
- Hagerty J
- Stanley R
- Stoecker W
- Publication year
- Publication venue
- Cancers
External Links
Snippet
Simple Summary Supervised deep learning techniques can now automatically process whole dermoscopic images and obtain a diagnostic accuracy for melanoma that exceeds that of specialists. These automatic diagnosis systems are now appearing in clinics …
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
- G06K9/36—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
- 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
- G06K9/62—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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
-
- 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
- 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
- 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
- G06Q50/01—Social networking
-
- 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
- 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
- G06K9/20—Image acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- 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
- 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
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Skin lesion analysis towards melanoma detection using deep learning network | |
Gao et al. | Hyperspectral image classification using convolutional neural networks and multiple feature learning | |
Hameed et al. | An intelligent computer-aided scheme for classifying multiple skin lesions | |
Tahoun et al. | A grey wolf-based method for mammographic mass classification | |
Raza et al. | Melanoma classification from dermoscopy images using ensemble of convolutional neural networks | |
Alsharif et al. | PneumoniaNet: Automated detection and classification of pediatric pneumonia using chest X-ray images and CNN approach | |
Roy et al. | Incremental dilations using CNN for brain tumor classification | |
Nambisan et al. | Improving automatic melanoma diagnosis using deep learning-based segmentation of irregular networks | |
Shehzad et al. | A deep-ensemble-learning-based approach for skin cancer diagnosis | |
Siddiqui et al. | Multi-class disease classification in brain MRIs using a computer-aided diagnostic system | |
Bakheet et al. | Computer-aided diagnosis of malignant melanoma using Gabor-based entropic features and multilevel neural networks | |
Abayomi-Alli et al. | Few-shot learning with a novel voronoi tessellation-based image augmentation method for facial palsy detection | |
Devi et al. | Gaussian blurring technique for detecting and classifying acute lymphoblastic leukemia cancer cells from microscopic biopsy images | |
Alawad et al. | AIBH: accurate identification of brain hemorrhage using genetic algorithm based feature selection and stacking | |
Hasan et al. | MRI brain classification using the quantum entropy LBP and deep-learning-based features | |
Nanni et al. | Deep features for training support vector machines | |
Peña et al. | Auto diagnostics of lung nodules using minimal characteristics extraction technique | |
Alturki et al. | Combining CNN features with voting classifiers for optimizing performance of brain tumor classification | |
Sun et al. | Tooth-marked tongue recognition using gradient-weighted class activation maps | |
Hammad et al. | Enhanced deep learning approach for accurate eczema and psoriasis skin detection | |
Bistroń et al. | Comparison of machine learning algorithms used for skin cancer diagnosis | |
Yu et al. | Synthesis of prostate MR images for classification using capsule network-based GAN model | |
Adegun et al. | A probabilistic-based deep learning model for skin lesion segmentation | |
Joshi et al. | Enhanced pre-trained xception model transfer learned for breast cancer detection | |
Zhu et al. | PointNet++ and three layers of features fusion for occlusion three-dimensional ear recognition based on one sample per person |