A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images

2021 ◽  
Author(s):  
Kriti ◽  
Jitendra Virmani ◽  
Ravinder Agarwal
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Cad Systems ◽  
Breast Tumor Classification

FMRNet: A fused network of multiple tumoral‐regions for breast tumor classification with ultrasound images

2021 ◽  
Author(s):  
Wenju Cui ◽  
Yunsong Peng ◽  
Gang Yuan ◽  
Weiwei Cao ◽  
Yuzhu Cao ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Breast Tumor Classification

Breast Tumor Classification of Ultrasound Images Using Wavelet-Based Channel Energy and ImageJ

2009 ◽  
Vol 3 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Hsieh-Wei Lee ◽  
Bin-Da Liu ◽  
King-Chu Hung ◽  
Sheau-Fang Lei ◽  
Po-Chin Wang ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Breast Tumor Classification

scholarly journals Breast tumor classification in ultrasound images using support vector machines and neural networks

2016 ◽  
Vol 32 (3) ◽  
pp. 283-292 ◽  
Author(s):  
Carmina Dessana Lima Nascimento ◽  
Sérgio Deodoro de Souza Silva ◽  
Thales Araújo da Silva ◽  
Wagner Coelho de Albuquerque Pereira ◽  
Marly Guimarães Fernandes Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Support Vector ◽  
Ultrasound Images ◽  
Vector Machines ◽  
Breast Tumor Classification

Breast tumor classification through learning from noisy labeled ultrasound images

2019 ◽  
Vol 47 (3) ◽  
pp. 1048-1057 ◽  
Author(s):  
Zhantao Cao ◽  
Guowu Yang ◽  
Qin Chen ◽  
Xiaolong Chen ◽  
Fengmao Lv
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Breast Tumor Classification

scholarly journals RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

2021 ◽  
Vol 11 (24) ◽  
pp. 12138
Author(s):  
Shahriar Mahmud Kabir ◽  
Mohammed I. H. Bhuiyan ◽  
Md Sayed Tanveer ◽  
ASM Shihavuddin
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Classification Performance ◽  
Tumor Classification ◽  
P Value ◽  
Ultrasound Images ◽  
Custom Made ◽  
Feature Based ◽  
Sensitivity Specificity ◽  
Breast Tumor Classification

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

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