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Dual hybridization method for the classification of ultrasound breast tumors

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mp.13361openalex-snapshot

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

inferredcites →https://doi.org/10.1007/978-0-387-30164-8_425openalex-snapshot

K-Means Clustering

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/978-981-13-2517-5_14openalex-snapshot

A Novel Breast Tumor Classification in Ultrasound Images, Using Deep Convolutional Neural Network

inferredcites →https://doi.org/10.1007/s10549-004-2043-zopenalex-snapshot

Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors

inferredcites →https://doi.org/10.1007/s11042-021-11240-0openalex-snapshot

An efficient deep neural network based abnormality detection and multi-class breast tumor classification

inferredcites →https://doi.org/10.1007/s11604-019-00831-5openalex-snapshot

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network

inferredcites →https://doi.org/10.1016/j.cmpb.2013.03.017openalex-snapshot

Computer-aided diagnosis of breast masses using quantified BI-RADS findings

inferredcites →https://doi.org/10.1016/j.dib.2019.104863openalex-snapshot

Dataset of breast ultrasound images

inferredcites →https://doi.org/10.1038/s41598-021-81008-xopenalex-snapshot

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

inferredcites →https://doi.org/10.1038/srep24454openalex-snapshot

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

inferredcites →https://doi.org/10.1109/cewit.2013.6713755openalex-snapshot

Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound

inferredcites →https://doi.org/10.1109/cvpr.2019.00419openalex-snapshot

Deep Spectral Clustering Using Dual Autoencoder Network

inferredcites →https://doi.org/10.1109/icabme47164.2019.8940291openalex-snapshot

Breast Cancer Classification in Ultrasound Images using Transfer Learning

inferredcites →https://doi.org/10.1109/isbi.2017.7950480openalex-snapshot

Classification of breast lesions using cross-modal deep learning

inferredcites →https://doi.org/10.1109/tfuzz.2013.2269149openalex-snapshot

Breast Nodules Computer-Aided Diagnostic System Design Using Fuzzy Cerebellar Model Neural Networks

inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

inferredcites →https://doi.org/10.1111/exsy.12713openalex-snapshot

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

inferredcites →https://doi.org/10.1148/radiol.13121606openalex-snapshot

Breast Ultrasonography: State of the Art

inferredcites →https://doi.org/10.1186/s12938-015-0022-8openalex-snapshot

Robust phase-based texture descriptor for classification of breast ultrasound images

inferredcites →https://doi.org/10.1186/s12938-019-0626-5openalex-snapshot

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

inferredcites →https://doi.org/10.3390/s20092717openalex-snapshot

A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification

inferredcites →https://doi.org/10.3390/s20236838openalex-snapshot

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3989658openalex-extracted

Unsupervised Learning Method Via Triple Reconstruction for the Classification of Ultrasound Breast Tumors

inferred← citeshttps://doi.org/10.1109/jbhi.2023.3331944openalex-snapshot

HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM

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