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Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science
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- 발행기관
- 보안공학연구지원센터(IJBSBT) 바로가기
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- 간행물
- International Journal of Bio-Science and Bio-Technology SCOPUS 바로가기
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- 통권
- Vol.8 No.6 (2016.12)바로가기
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- 페이지
- pp.93-106
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A294355
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- There are many medical imaging modalities used for the analysis and cure of various diseases. One of the most important of these modalities is Magnetic Resonance Imaging (MRI). MRI is advantageous over other modalities due to its high spatial resolution and the excellent capability of discrimination of soft tissues. In this paper, an automated classification approach of normal and pathological MRI is proposed. The proposed model three simple stages; preprocessing, feature extraction and classification. Two types of features; color moments and texture features have been considered as main features for the description of brain MRI. A probabilistic classifier based on logistic function has been used for the MRI classification. A standard data set consisting of one hundred and fifty images has been used in the experiments, which was divided into 66% training and 34% testing. The proposed approach gave 98% accurate results for training data set and 94% accurate results for the testing data set. For validation of the proposed approach, 10-Fold cross validation was applied, which gave 90.66% accurate results. The classification capability of probabilistic classifier has been compared with the different state of art classifiers, including Support Vector Machine (SVM), Naïve Bayes, Artificial Neural Network (ANN), and Normal densities based linear classifier.
목차
Abstract
1. Introduction
2. Related Work
3. Proposed Methodology
3.1. Data Set Description
3.2. MRI Pre-Processing
3.3. MRI Feature Extraction
3.4. MRI Classification
4. Experimental Results and Discussion
4.1. Algorithm Accuracy
4.2. Comparative Analysis
5. Conclusion and Future Work
References
키워드
Brain MRI Classification Color Features Probabilistic Classifier Pathological MRI Texture Features저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJBSBT) [Science & Engineering Research Support Center, Republic of Korea(IJBSBT)]
- 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
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- 간행물명
- International Journal of Bio-Science and Bio-Technology
- 간기
- 격월간
- pISSN
- 2233-7849
- 수록기간
- 2009~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Bio-Science and Bio-Technology Vol.8 No.6
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