NDLI: Classification of breast tumours on ultrasound images using morphometric parameters

Content Provider	IEEE Xplore Digital Library
Author	Alvarenga, A.V. Pereira, W.C.A. Infantosi, A.F.C. de Azevedo, C.M.
Copyright Year	2005
Description	Author affiliation: Biomedical Eng. Program, Fed. Univ. of Rio de Janeiro, Brazil (Alvarenga, A.V.; Pereira, W.C.A.; Infantosi, A.F.C.)
Abstract	This work aims to assess the potentiality of morphometric parameters in separating breast tumour, on ultrasonic images, as malign or benign. Parameters were calculated over normalised radial length and convex polygons from 152 segmented tumour images. Linear discriminant analysis was applied and parameters performance assessed (accuracy, sensitivity and specificity). The best parameter performances for individual parameters were the normalised residual mean square value and the circularity. Taking these last two and the roughness the best separation performance was obtained: specificity (90.4%) and sensitivity (88.0%). These three parameters were also applied to a multilayer perceptron network using GA-backpropagation hybrid training. The initial results pointed out that hybrid GA-backpropagation training was capable to produce similar high performance both to training (accuracy = 90.3%, sensitivity = 90.0% and specificity = 90.9%) and test (accuracy, sensitivity and specificity equal to 90.0%) procedures. Besides, the performances obtained with two training sets of distinct sizes (30% and 50% of all samples) were slightly different.
Starting Page	206
Ending Page	210
File Size	1138062
Page Count	5
File Format	PDF
ISBN	078039030X
e-ISBN	0780390318
DOI	10.1109/WISP.2005.1531659
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-01
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Breast tumors Ultrasonic imaging Image databases Biopsy Artificial neural networks Sensitivity and specificity Multilayer perceptrons Breast cancer Mammography Genetic algorithms
Content Type	Text
Resource Type	Article

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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
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