NDLI: A post-processing method for EISI to distinguish breast lesions

Content Provider	IEEE Xplore Digital Library
Author	Feng Zhang Shipeng Xie Fei Hu Limin Luo Xudong Bao
Copyright Year	2010
Description	Author affiliation: Lab of Image Science and Technology, Southeast University, Nanjing, China (Feng Zhang; Shipeng Xie; Fei Hu; Limin Luo; Xudong Bao)
Abstract	To increase the accuracy of Electrical Impedance Scanning Imaging (EISI) on inspecting breast diseases, reduce the false positive and false negative, we propose a method to use Independent Component Analysis (ICA) to extract features from the original admittance data of EISI equipments. Combined with Support Vector Machine (SVM) as classifier, the proposed method could inspect breast diseases and give nonuser-dependent diagnosis results. Experimental results suggest that the features extracted by ICA are effective in classifying breast diseases. To recognize lesions within breast under inspect which is malignant or benign, the proposed method could achieve a sensitivity of 74.2% and a specificity of 82.8%. Accuracy is calculated as 80%. Combined the proposed method with regular EISI inspecting process could help to increase the reliability of EISI, avert benign biopsies.
Starting Page	704
Ending Page	707
File Size	305320
Page Count	4
File Format	PDF
ISBN	9781424464951
e-ISBN	9781424464982
DOI	10.1109/BMEI.2010.5640067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Sensitivity independent component analysis support vector machine Breast breast cancer electrical impedance scanning imaging Lesions Impedance Cancer
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Breast cancer classification by using support vector machines with reduced dimension

A new support vector machine method for medical image classification

Machine learning for noise removal on breast ultrasound images

Diagnosis of Breast Cancer Tumor Based on ICA and LS-SVM

Independent Component Analysis as an Effective Tool for Automated Diagnosis of Melanoma

New Method Based on Support Vector Machine in Classification for Hyperspectral Data

Brain decoding of texture processing using independent component analysis and support vector machines

Independent Component Analysis Applied to Ultrasound Speckle Texture Analysis and Tissue Characterization

Application of combined support vector machines in process fault diagnosis

A post-processing method for EISI to distinguish breast lesions

Similar Documents

Breast cancer classification by using support vector machines with reduced dimension

A new support vector machine method for medical image classification

Machine learning for noise removal on breast ultrasound images

Diagnosis of Breast Cancer Tumor Based on ICA and LS-SVM

Independent Component Analysis as an Effective Tool for Automated Diagnosis of Melanoma

New Method Based on Support Vector Machine in Classification for Hyperspectral Data

Brain decoding of texture processing using independent component analysis and support vector machines

Independent Component Analysis Applied to Ultrasound Speckle Texture Analysis and Tissue Characterization

Application of combined support vector machines in process fault diagnosis

A post-processing method for EISI to distinguish breast lesions