Reszke et al., 2023 - Google Patents
Machine learning methods in the detection of brain tumorsReszke et al., 2023
View PDF- Document ID
- 7256588204386549216
- Author
- Reszke M
- Smaga Å
- Publication year
- Publication venue
- Biometrical Letters
External Links
Snippet
Brain tumor is a very serious disease from which many people die every day. Appropriate early diagnosis is extremely important in treatment. In recent years, machine learning methods have come to the aid of doctors, allowing them to automate the process of brain …
- 208000003174 Brain Neoplasms 0 title abstract description 44
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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ukwuoma et al. | A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images | |
Tazin et al. | [Retracted] A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network | |
Novitasari et al. | Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network | |
Jayalakshmi et al. | Performance analysis of convolutional neural network (CNN) based cancerous skin lesion detection system | |
Ukwuoma et al. | Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images | |
Tavana et al. | Classification of spinal curvature types using radiography images: deep learning versus classical methods | |
Kalaivani et al. | Detection and classification of skin diseases with ensembles of deep learning networks in medical imaging | |
Vinod et al. | Ensemble Technique for Brain Tumour Patient Survival Prediction | |
Karacı et al. | YoDenBi-NET: YOLO+ DenseNet+ Bi-LSTM-based hybrid deep learning model for brain tumor classification | |
Karthik et al. | Ensemble-based multimodal medical imaging fusion for tumor segmentation | |
Binol et al. | A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification | |
Chandra et al. | Detection of brain tumors from MRI using gaussian RBF kernel based support vector machine | |
Perkonigg et al. | Detecting bone lesions in multiple myeloma patients using transfer learning | |
Reszke et al. | Machine learning methods in the detection of brain tumors | |
Mizan et al. | A comparative study of tuberculosis detection using deep convolutional neural network | |
Bodapati | Enhancing brain tumor diagnosis using a multi-architecture deep convolutional neural network on MRI scans | |
Pandey et al. | Skin cancer classification using non-local means denoising and sparse dictionary learning based CNN | |
Kukreti et al. | Detection and Classification of Brain Tumour Using EfficientNet and Transfer Learning Techniques | |
Sadeghi et al. | A Novel Sep-Unet architecture of convolutional neural networks to improve dermoscopic image segmentation by training parameters reduction | |
Maqsood et al. | Artificial intelligence-based classification of CT images using a Hybrid SpinalZFNet | |
Rajesh et al. | A Deep Learning Approach for Classification of Vitiligo and Scar Images | |
Harika et al. | Classification of Cervical Cancer using ResNet-50 | |
Maloji | Optimised ResNet50 for Multi-Class Classification of Brain Tumors | |
KUNDRA et al. | SKIN LESION CLASSIFICATION FOR MELANOMA USING DEEP LEARNING | |
Giuri et al. | CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis |