Nayak et al., 2020 - Google Patents
Firefly algorithm in biomedical and health care: advances, issues and challengesNayak et al., 2020
View HTML- Document ID
- 16204265561756061882
- Author
- Nayak J
- Naik B
- Dinesh P
- Vakula K
- Dash P
- Publication year
- Publication venue
- SN Computer Science
External Links
Snippet
Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient …
- 241000254158 Lampyridae 0 title abstract description 175
Classifications
-
- 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
- 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
- G06K9/6267—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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- 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
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nayak et al. | Firefly algorithm in biomedical and health care: advances, issues and challenges | |
Suganyadevi et al. | A review on deep learning in medical image analysis | |
Ker et al. | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans | |
Ashraf et al. | Deep transfer learning for alzheimer neurological disorder detection | |
Pires et al. | A data-driven approach to referable diabetic retinopathy detection | |
Helwan et al. | Deep networks in identifying CT brain hemorrhage | |
Balaji et al. | Hybridized deep learning approach for detecting Alzheimer’s disease | |
Ali et al. | Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image | |
Lu et al. | A method for optimal detection of lung cancer based on deep learning optimized by marine predators algorithm | |
Mohammed et al. | Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID‐19 Diagnostic Model | |
Bhuvaneswari et al. | A new fusion model for classification of the lung diseases using genetic algorithm | |
Jain et al. | Lung nodule segmentation using salp shuffled shepherd optimization algorithm-based generative adversarial network | |
Chudzik et al. | Exudate segmentation using fully convolutional neural networks and inception modules | |
Feng et al. | Supervoxel based weakly-supervised multi-level 3D CNNs for lung nodule detection and segmentation | |
Rashed et al. | Critical analysis of the current medical image-based processing techniques for automatic disease evaluation: systematic literature review | |
Shamrat et al. | Analysing most efficient deep learning model to detect COVID-19 from computer tomography images | |
Mareeswari et al. | A narrative review of medical image processing by deep learning models: origin to COVID-19 | |
Goyal et al. | Musculoskeletal abnormality detection in medical imaging using GnCNNr (group normalized convolutional neural networks with regularization) | |
Jatmiko et al. | A review of big data analytics in the biomedical field | |
Rajan et al. | IoT based optical coherence tomography retinal images classification using OCT Deep Net2 | |
Ozaltin et al. | Artificial intelligence-based brain hemorrhage detection | |
Mandal et al. | Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches | |
Ganesh et al. | Multi class robust brain tumor with hybrid classification using DTA algorithm | |
Kumar et al. | Prostate cancer classification with MRI using Taylor-Bird squirrel optimization based deep recurrent neural network | |
Prasad et al. | Fractional Pelican African Vulture Optimization-based classification of breast cancer using mammogram images |