NDLI: A methodology of computer aided diagnostic system on breast cancer

Content Provider	IEEE Xplore Digital Library
Author	Hee-Jun Song Seon-Gu Lee Gwi-Tae Park
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Korea Univ., Seoul (Hee-Jun Song; Seon-Gu Lee; Gwi-Tae Park)
Abstract	In this paper, a new approach using ANFIS (adaptive neuro-fuzzy inference system) as a diagnosis system on Wisconsin breast cancer diagnosis (WBCD) problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with a human like decision-making process using artificial intelligence (AI) algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data by itself. Considering these features, applying ANFIS as a diagnostic system was considered in our experiment. In addition, in real implementations, the performance of diagnosis system in computation is an important issue as well as the correctness of the output from the inference system. A couple of methods using recommended inputs generated by genetic algorithm, decision tree and correlation coefficient computation with ANFIS are proposed to reduce the computational overhead and they possibly enhance the performance by eliminating less-relevant input features
Starting Page	831
Ending Page	836
File Size	501779
Page Count	6
File Format	PDF
ISBN	0780393546
DOI	10.1109/CCA.2005.1507232
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Breast cancer Artificial intelligence Medical diagnostic imaging Inference algorithms Adaptive systems Humans Decision making Fuzzy neural networks Fuzzy systems Artificial neural networks
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

A hybrid system for nodal involvement assessment in breast cancer patients

Computer-aided diagnosis of breast cancer using artificial neural networks: comparison of backpropagation and genetic algorithms

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology

An expert sytem for diagnosis breast cancer based on Principal Component Analysis method

Medical decision making systems in pulmonology: a creative environment based on artificial neural networks

A Fuzzy Inference System Approach for Knowledge Management Tools Evaluation

Artificial Ventilation Modeling using Neuro-Fuzzy Hybrid System

A methodology of computer aided diagnostic system on breast cancer

Similar Documents

A hybrid system for nodal involvement assessment in breast cancer patients

Computer-aided diagnosis of breast cancer using artificial neural networks: comparison of backpropagation and genetic algorithms

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology

An expert sytem for diagnosis breast cancer based on Principal Component Analysis method

Medical decision making systems in pulmonology: a creative environment based on artificial neural networks

A Fuzzy Inference System Approach for Knowledge Management Tools Evaluation

Artificial Ventilation Modeling using Neuro-Fuzzy Hybrid System

A methodology of computer aided diagnostic system on breast cancer