scholarly journals Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging

2015 ◽  
Vol 46 ◽  
pp. 1601-1609 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Kesari Verma ◽  
A.S. Thoke
Keyword(s):  
Ultrasound Imaging ◽  
Gradient Descent ◽  
Breast Tumors

scholarly journals Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 182 ◽  
Author(s):  
Yali Ouyang ◽  
Po-Hsiang Tsui ◽  
Shuicai Wu ◽  
Weiwei Wu ◽  
Zhuhuang Zhou
Keyword(s):  
Ultrasound Imaging ◽  
Hermite Polynomials ◽  
Relative Size ◽  
Speckle Noise ◽  
Breast Tumors ◽  
New Method ◽  
Ultrasound Images ◽  
Ultrasound Technique ◽  
Malignant Breast

Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.

scholarly journals Feature Extraction from a Signature Based on the Turning Angle Function for the Classification of Breast Tumors

2007 ◽  
Vol 21 (2) ◽  
pp. 129-144 ◽  
Author(s):  
Denise Guliato ◽  
Juliano D. de Carvalho ◽  
Rangaraj M. Rangayyan ◽  
Sérgio A. Santiago
Keyword(s):  
Feature Extraction ◽  
Breast Tumors ◽  
Turning Angle ◽  
Angle Function

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

2021 ◽  
Vol 77 ◽  
pp. 86-91
Author(s):  
Ann L. Brown ◽  
Joanna Jeong ◽  
Rifat A. Wahab ◽  
Bin Zhang ◽  
Mary C. Mahoney
Keyword(s):  
Diagnostic Accuracy ◽  
Breast Tumors ◽  
Textural Analysis

Real-Time Classification of Hand Motions Using Ultrasound Imaging of Forearm Muscles

2016 ◽  
Vol 63 (8) ◽  
pp. 1687-1698 ◽  
Author(s):  
Nima Akhlaghi ◽  
Clayton A. Baker ◽  
Mohamed Lahlou ◽  
Hozaifah Zafar ◽  
Karthik G. Murthy ◽  
...  
Keyword(s):  
Real Time ◽  
Ultrasound Imaging ◽  
Forearm Muscles ◽  
Real Time Classification

scholarly journals Classification of benign and malignant breast tumors on the basis of 36 radiographic properties

Cancer ◽  
1973 ◽  
Vol 31 (2) ◽  
pp. 342-352 ◽  
Author(s):  
Laurens V. Ackerman ◽  
Anthony N. Mucciardi ◽  
Earl E. Gose
Keyword(s):  
Breast Tumors ◽  
Malignant Breast

Classification of Breast Tumors in Digitized Mammograms

2003 ◽  
pp. 382-386
Author(s):  
C. Varela ◽  
N. Karssemeijer ◽  
J. M. Muller ◽  
P. G. Tahoces
Keyword(s):  
Breast Tumors

scholarly journals Correction to: Histological classification of breast tumors in the General Rules for Clinical and Pathological Recording of Breast Cancer (18th edition)

Breast Cancer ◽  
2020 ◽  
Vol 27 (4) ◽  
pp. 792-792
Author(s):  
Hitoshi Tsuda ◽  
Koichiro Tsugawa ◽  
Futoshi Akiyama ◽  
Rie Horii ◽  
Masafumi Kurosumi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tumors ◽  
Histological Classification ◽  
General Rules
Sign in / Sign up

Export Citation Format

Share Document