Density based Multiclass Support Vector Machine using IoT driven Service Oriented Architecture for Predicting Cervical Cancer
-
- 발행기관
- 보안공학연구지원센터(IJUNESST) 바로가기
-
- 간행물
- International Journal of u- and e- Service, Science and Technology 바로가기
-
- 통권
- Vol.9 No.11 (2016.11)바로가기
-
- 페이지
- pp.195-216
-
- 저자
-
- 언어
- 영어(ENG)
-
- URL
- https://www.earticle.net/Article/A293291
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Cervical Cancer stands out among those deadliest diseases, which threatens women in an alarming rate causing approximately 2, 66,000 mortalities per annum worldwide. This cancer can be diagnosed early enough through Pap smear test; a cervical cancer screening program. Finding out the true positive rates of the Cervical Cancer cells with precision is more complex when identifying the same categories of the cancer disease. Various researchers have proposed many approaches over the past four decades and the solutions are pertinent to cervical cancer; however, the challenge remains partially unresolved. The significant contribution of this paper is in two folds, firstly discuss a cloud ready Adapter Driven Service Oriented Architecture (RESPRO 3.0), developed by us for automated screening of Pap Smear can be extended to any International Classification of Diseases (ICD). Secondly, present an Internet of Things (IoT) driven Cervical Cancer prediction adapter built for RESPRO 3.0 based on Density based Multiclass Support Vector Machines (MCSVM) in combination with Polynomial Kernel Trick. The density parameters provide unique space in identifying cervical cancer cell categories compared to exising researches. This cloud solution’s results are bench marked and verified against cyto technician’s ground truth results, found to be highly satisfactory with respect to 93% Sensitivity and 99% Specificity while minimizing test repeatability ratio for the supervised training set of images.
목차
Abstract
1. Introduction
2. Related Work
3.2. Bethesda System
4. IoT Driven Clinical Analysis Architecture (IDCAA)
4.1. Service Oriented Architecture
4.2. Architecture
4.3. Automation Stages and Ranking Features
5. Support Vector Machines (SVMs)
5.1. Binary Classification Using SVM
5.2. Multiclass Support Vector Machine (MSVM)
5.3. Proposed Density based Multi Class Support Vector Machine(DMCSVM)
6. Experimental Classification Results and Analysis
6.1. Image Pre Processing and Preparation for Training Set
6.2. Pap Smear Image Processing through Multi Class Support Vector Machine
6.3. Performance Assessment
7. Conclusion and Suggestions
References
키워드
Cervical Cancer Density based Support Vector Machine DSVM Service Oriented Architecture SOA Kernel Functions Polynomial Kernel Trick저자
간행물 정보
발행기관
-
- 발행기관명
- 보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
-
- 간행물명
- International Journal of u- and e- Service, Science and Technology
- 간기
- 격월간
- pISSN
- 2005-4246
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.9 No.11
피인용수 : 0건 (자료제공 : 네이버학술정보)
함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.
0개의 논문이 장바구니에 담겼습니다.