Diame et al., 2021 - Google Patents
Deep learning architiectures for aided melanoma skin disease recognition: a reviewDiame et al., 2021
- Document ID
- 9735222247290600398
- Author
- Diame Z
- Al-Berry M
- Salem M
- Roushdy M
- Publication year
- Publication venue
- 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)
External Links
Snippet
Melanoma, a sort of skin disease, in spite of the fact that it represents a little percentage of skin malignancies in the USA, it represents higher than seventy five percent of all skin diseases connected to all fatalities in the USA alone. This motivated researcher to seek …
- 206010025650 Malignant melanoma 0 title abstract description 13
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
- G06K9/36—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
- 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
- G06K9/62—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
- 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
-
- 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
- G06K9/00006—Acquiring or recognising fingerprints or palmprints
-
- 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
- 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 |
---|---|---|
Shorfuzzaman | An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection | |
Adegun et al. | Deep learning-based system for automatic melanoma detection | |
Pomponiu et al. | Deepmole: Deep neural networks for skin mole lesion classification | |
CN108830326B (en) | Automatic segmentation method and device for MRI (magnetic resonance imaging) image | |
Chan et al. | Texture-map-based branch-collaborative network for oral cancer detection | |
Diame et al. | Deep learning architiectures for aided melanoma skin disease recognition: a review | |
Pan et al. | Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks | |
CN106570505A (en) | Method for analyzing histopathologic image and system thereof | |
KR102689569B1 (en) | Method and apparatus for classifying a medical image | |
Sornapudi et al. | EpithNet: Deep regression for epithelium segmentation in cervical histology images | |
Reenadevi et al. | Breast cancer histopathological image classification using augmentation based on optimized deep ResNet-152 structure | |
Naeem et al. | DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images | |
Huang et al. | Automatic Retinal Vessel Segmentation Based on an Improved U‐Net Approach | |
Bai et al. | A scalable graph-based framework for multi-organ histology image classification | |
CN113902702A (en) | An auxiliary diagnosis system for benign and malignant pulmonary nodules based on computed tomography | |
Manikandan et al. | Segmentation and detection of pneumothorax using deep learning | |
CN113744215A (en) | Method and device for extracting center line of tree-shaped lumen structure in three-dimensional tomography image | |
CN113313698A (en) | Pulmonary nodule detection method and device based on neural network and image processing equipment | |
Sharanyaa et al. | DCNET: a novel implementation of gastric cancer detection system through deep learning convolution networks | |
Alghanimi et al. | CNN and ResNet50 Model Design for Improved Ultrasound Thyroid Nodules Detection | |
Al‐Huda et al. | Weakly supervised skin lesion segmentation based on spot‐seeds guided optimal regions | |
Mathialagan et al. | Analysis and classification of H&E-stained oral cavity tumour gradings using convolution neural network | |
Babu et al. | Optimized deep learning for skin lesion segmentation and skin cancer detection | |
Hsu et al. | Contour extraction in medical images using initial boundary pixel selection and segmental contour following | |
Halder et al. | Morphological filter aided gmm technique for lung nodule detection |