Aguirre Nilsson et al., 2018 - Google Patents
Classification of ulcer images using convolutional neural networksAguirre Nilsson et al., 2018
View PDF- Document ID
- 6435234251193172482
- Author
- Aguirre Nilsson C
- Velic M
- Publication year
External Links
Snippet
The use of artificial intelligence has increased within a variety of different fields the last decade, including the area of health care. Machine learning algorithms have already been successfully used in eg skin cancer detection in images, indicating its potential for being …
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
-
- 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
- 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
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- 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
- 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
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- 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 |
---|---|---|
Biswas et al. | State-of-the-art review on deep learning in medical imaging | |
Liu et al. | Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images | |
Zhang et al. | Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons | |
Zhang et al. | A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm | |
Hu et al. | AS-Net: Attention Synergy Network for skin lesion segmentation | |
Kotia et al. | Few shot learning for medical imaging | |
Santosh et al. | Deep learning models for medical imaging | |
Niyaz et al. | Advances in deep learning techniques for medical image analysis | |
Khan et al. | Knowledge distillation approach towards melanoma detection | |
Aguirre Nilsson et al. | Classification of ulcer images using convolutional neural networks | |
Hampiholi | Medical Imaging Enhancement with Ai Models for Automatic Disease Detection and Classification Based on Medical Images | |
Riaz et al. | A comprehensive joint learning system to detect skin cancer | |
Huang et al. | Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer | |
Üzen | Convmixer-based encoder and classification-based decoder architecture for breast lesion segmentation in ultrasound images | |
Sankari et al. | Automated detection of retinopathy of prematurity using quantum machine learning and deep learning techniques | |
Sharma et al. | Solving image processing critical problems using machine learning | |
Hadi et al. | Comparison Between Convolutional Neural Network CNN and SVM in Skin Cancer Images Recognition | |
Selvia et al. | Skin lesion detection using feature extraction approach | |
Baskaran et al. | MSRFNet for skin lesion segmentation and deep learning with hybrid optimization for skin cancer detection | |
Akella et al. | An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images | |
Xin et al. | Transformer guided self-adaptive network for multi-scale skin lesion image segmentation | |
Khani | Medical image segmentation using machine learning | |
Farea et al. | A hybrid deep learning skin cancer prediction framework | |
Niranjana et al. | Enhanced Skin Diseases Prediction using DenseNet-121: Leveraging Dataset Diversity for High Accuracy Classification | |
Anisuzzaman | Novel Deep Neural Network for Medical Image Classification |