VCU School of Engineering Research Magazine, Vol. 5
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A Literature Survey on Computer-Aided Diagnosis in Detection and Classification of Polyp in Colon Cancer Using CT Colonography
ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Literature Survey on Computer-Aided Diagnosis in Detection and Classification of Polyp in Colon Cancer using CT Colonography Akshay Godkhindi 1, Dr Dayananda P 2, Dr Sowmyarani C N 3 MSRIT, Bangalore, India 1 Assistant Professor, Dept of Information Science & Engg, MSRIT, Bangalore, India 2 Associate Professor, Dept of Computer Science & Engg, RVCE, Bangalore, India 3 Abstract: Colorectal cancer is a cancer that starts inside the colon or the rectum in large intestine. These cancers is likewise called colon cancer or rectal cancer, depending on wherein they start. Colon and rectal cancer are frequently grouped together because they've many features in common and most colorectal cancers begin as a increase on the internal lining of the colon or rectum called a polyp. A few types of polyps can change into cancer over the several years, but not all polyps end up in cancers. The risk of changing into a most cancers depends at the kind of polyp. Computer-aided detection (CADe) and analysis (CAD) has been a rapidly growing, potential area of research in medical imaging. Machine leaning (ML) plays a crucial role in CAD, because objects such as lesions and organs may not be represented accurately with the aid of an easy equation; as a consequence, medical pattern recognition essentially require “getting to know from examples.” Computed tomography (CT) Colonography or virtual colonoscopy makes use of special x-ray machine to have a look at the large intestine for cancer and growths known as polyps. -
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 8, AUGUST 2013 1863 Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach Yuichi Motai, Senior Member, IEEE, and Hiroyuki Yoshida, Member, IEEE Abstract—Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace -
Data Mining Based Soft Computing Methods for Web Intelligence
International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected] Volume 3, Issue 3, March 2014 ISSN 2319 - 4847 DATA MINING BASED SOFT COMPUTING METHODS FOR WEB INTELLIGENCE Mr. Ankit R. Deshmukh1, Prof. Sunil R. Gupta2 1ME (CSE) ,First Year,Department of CSE,Prof. Ram Meghe Institute Of Technology and Research, Badnera,Amravati. Sant Gadgebaba Amravati University, Amarvati, Maharashtra, India - 444701 2Assitantant Professor, Department of CSE, Prof. Ram Meghe Institute Of Technology and Research, Badnera,Amravati. Sant Gadgebaba Amravati University, Amarvati, Maharashtra, India - 444701 Abstract Web has become the primary means for information distribution. It is being used for commercial, entertainment or educational purposes and thus its popularity resulted in heavy traffic in the Internet. Web Intelligence (WI) deals with the scientific exploration of the new territories of the Web. As a new field of computer science, it combines artificial intelligence and advanced information technology in the context of the Web, and goes beyond each of them. Data mining has a lot of scope in e- Applications. The key problem is how to find useful hidden patterns for better application. Problem to address soft computing techniques like Neural networks, Fuzzy Logic, Support Vector Machines, Genetic Algorithms in Evolutionary Computation. In this paper, we explore soft computing techniques use to achieve web intelligences. Keywords: Web, Web intelligence, Data mining, Soft computing, Neural networks, Support Vector Machines, Fuzzy Logic, Genetic Algorithm. 1. INTRODUCTION Data mining has useful business applications such as finding useful hidden information from databases, predicting future trends, and making good business decisions [1,6,7].