Korjenevsky, 2024 - Google Patents

Use of machine learning to diagnose breast cancer from raw electrical impedance tomography data

Korjenevsky, 2024

Document ID
8227749382297911063
Author
Korjenevsky A
Publication year
Publication venue
Biomedical Engineering

External Links

Snippet

The aim of the present work was to study the use of support vector machines to create an automatic physician assistant for three-dimensional (3D) electrical impedance tomography (EIT) of the breast. This work showed that machine learning based on the linear support …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0536Impedance imaging, e.g. by tomography
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0033Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts

Similar Documents

Publication Publication Date Title
Gautam et al. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN
Arun et al. Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging
Acharya et al. Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques
Abreu et al. EEG microstates predict concurrent fMRI dynamic functional connectivity states
Ashinsky et al. Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative
Walger et al. Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice
Rojas et al. Application of empirical mode decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease
Yang et al. Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial
Chirra et al. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI
Al-Ayyoub et al. A gpu-based breast cancer detection system using single pass fuzzy c-means clustering algorithm
Battalapalli et al. An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices
Giraldo et al. Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease
Çelebi et al. Leveraging Deep Learning for Enhanced Detection of Alzheimer's Disease Through Morphometric Analysis of Brain Images.
Kadhim et al. Classification and predictive diagnosis earlier Alzheimer’s disease using MRI brain images
Jha et al. Alzheimer disease detection in MRI using curvelet transform with KNN
Yu et al. Multi‐scale, domain knowledge‐guided attention+ random forest: a two‐stage deep learning‐based multi‐scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images
Magherini et al. Distinguishing kidney tumor types using radiomics features and deep features
Siddiqui et al. Artificial intelligence-based myocardial infarction diagnosis: a comprehensive review of modern techniques
Mohapatra et al. Meta-analysis of transfer learning for segmentation of brain lesions
Korjenevsky Use of machine learning to diagnose breast cancer from raw electrical impedance tomography data
Anitha et al. Adhd classification from fmri data using fine tunining in svm
Ali Al-Hamza Vit-bt: Improving mri brain tumor classification using vision transformer with transfer learning
Kruthika et al. Classification of alzheimer and MCI phenotypes on MRI data using SVM
Bahadori et al. Diagnosing Alzheimer's Disease Levels Using Machine Learning and MRI: A Novel Approach
Azilinon et al. Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy