NDLI: Data mining techniques for AFM- based tumor classification

Content Provider	IEEE Xplore Digital Library
Author	Hutterer, S. Zauner, G. Huml, M. Silye, R. Schilcher, K.
Copyright Year	2012
Description	Author affiliation: School of Applied Health and Social Sciences, Linz, of the University of Applied Sciences Upper Austria, Austria (Huml, M.; Schilcher, K.) \|\| Neuropathic Hospital Wagner-Jauregg Department of Pathology, Linz, Austria (Silye, R.) \|\| School of Engineering and Environmental Sciences, Wels, Austria (Hutterer, S.; Zauner, G.)
Abstract	The present paper deals with the application of atomic force microscopy (AFM) as a tool for morphological characterization of histological brain tumor samples. Data mining techniques will be applied for automatic identification of brain tumor tissues based on AFM images by means of classifying grade II and IV tumors. The rapid advancement of AFM in recent years turned it into a valuable and useful tool to determine the topography of surface nanoscale structures with high precision. Therefore, it is used in a variety of applications in life science, materials science, electrochemistry, polymer science, biophysics, nanotechnology, and biotechnology. Minkowski functionals are used (in particular the Euler- Poincaré characteristic) as a feature descriptor to characterize global geometric structures in images related to the topology of the AFM image. In order to improve classification accuracy on the one hand, but to infer interpretable information from AFM images for domain experts on the other hand, feature analysis and reduction will be applied. From a data mining point of view, Genetic Programming will be introduced as a sophisticated method for both feature analysis and reduction as well as for producing highly accurate and interpretable models. Support Vector Machines will be used for comparison reasons when talking about reachable model accuracy.
Starting Page	105
Ending Page	111
File Size	1682386
Page Count	7
File Format	PDF
ISBN	9781467311908
e-ISBN	9781467311915
DOI	10.1109/CIBCB.2012.6217218
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Data mining Feature extraction Support vector machines Accuracy Genetic programming Brain modeling Genetic Programming Atomic Force Microscopy Tumor Classification
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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