Yin et al., 2019 - Google Patents

Hybrid regional feature cutting network for thyroid ultrasound images classification

Yin et al., 2019

Document ID
15481827870418758551
Author
Yin J
Song R
Wang J
Zhu C
Yang S
Yang J
Publication year
Publication venue
2019 IEEE 5th International conference on computer and communications (ICCC)

External Links

Snippet

With the increasing incidence of thyroid diseases, the detection and classification of thyroid nodule has attracted more and more attention. Deep learning has achieved promising results in computer-aided diagnosis due to the advantages of obtaining high-dimensional …
Continue reading at ieeexplore.ieee.org (other versions)

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
    • G06K9/36—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
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • 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
    • G06K9/6267—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
    • G06T2207/30048—Heart; Cardiac
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • 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
    • 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
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Mall et al. A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Brunetti et al. Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images
Yu et al. Crossbar-net: A novel convolutional neural network for kidney tumor segmentation in ct images
Zhou et al. Lung cancer cell identification based on artificial neural network ensembles
Qu et al. Deep learning-based methodology for recognition of fetal brain standard scan planes in 2D ultrasound images
Chen et al. Discriminative cervical lesion detection in colposcopic images with global class activation and local bin excitation
WO2021183765A1 (en) Automated detection of tumors based on image processing
CN113408603B (en) Coronary artery stenosis degree identification method based on multi-classifier fusion
CN114398979A (en) Ultrasonic image thyroid nodule classification method based on feature decoupling
Han et al. Fundus retinal vessels image segmentation method based on improved U-Net
Akkar et al. Diagnosis of lung cancer disease based on back-propagation artificial neural network algorithm
Liu et al. Automated classification of cervical Lymph-Node-Level from ultrasound using depthwise separable convolutional swin transformer
Ding et al. A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images
Li et al. Convolutional feature descriptor selection for mammogram classification
Saumiya et al. Unified automated deep learning framework for segmentation and classification of liver tumors
Yin et al. Hybrid regional feature cutting network for thyroid ultrasound images classification
Lu et al. Collaborative multi-metadata fusion to improve the classification of lumbar disc herniation
Le Minh et al. Deep feature fusion for breast cancer diagnosis on histopathology images
Chugh et al. Morphological and otsu’s technique based mammography mass detection and deep neural network classifier based prediction
Rani et al. Computer-aided Bio-medical Tools for Disease Identification
Chehade et al. Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14
Mandle et al. Intelligent brain tumor detection system using deep learning technique
Ju et al. CDI-NSTSEG: A clinical diagnosis-inspired effective and efficient framework for non-salient small tumor segmentation
Mao et al. Studies on Category Prediction of Ovarian Cancers Based on Magnetic Resonance Images
Han et al. Deep Positional-Representation-Based Local Information Retention Networks for Mammography Classification