Intelligent Brain Tumor Lesion Classification and Identification from MRI Images Using k-NN Technique

被引：0
|
作者：
Sudharani, K. [1 ]
Sarma, T. C. [2 ]
Rasad, K. Satya [3 ]
机构：
[1] VNR Vignana Jyothi IET, Hyderabad, Telangana, India
[2] NRSA, Hyderabad, Telangana, India
[3] JNTU Kakinada, Kakinada, Andhra Prades, India
来源：
2015 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT) | 2015年
关键词：
MRI scan; CT; k-NN; LabVIEW; Identification score and classification score; Manhattan distance metric;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) provides scanned images for Brain Tumor detection. Growth of abnormal cells in uncontrolled manner is tumor. The present paper proposed the classification and identification scores of brain tumor by using k-NN algorithm which is based on training of k. In this work Manhattan metric has applied and calculated the distance of the classifier. The algorithm has been implemented using the Lab View.
引用
收藏
页码：777 / 780
页数：4
相关论文
共 49 条
  • [1] Robust Classification of Primary Brain Tumor in Computer Tomography Images Using K-NN and Linear SVM
    Sundararaj, G. Kharmega
    Balamurugan, V.
    2014 INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), 2014, : 1315 - 1319
  • [2] Classification in medical images using adaptive metric k-NN
    Chen, C.
    Chernoff, K.
    Karemore, G.
    Lo, P.
    Nielsen, M.
    Lauze, F.
    MEDICAL IMAGING 2010: IMAGE PROCESSING, 2010, 7623
  • [3] UNIVERSAL k-NN (UNN) CLASSIFICATION OF CELL IMAGES USING HISTOGRAMS OF DoG COEFFICIENTS
    Piro, Paolo
    Ali, Wafa Bel Haj
    Crescence, Lydie
    Ferhat, Omelkheir
    Darcourt, Jacques
    Pourcher, Thierry
    Barlaud, Michel
    BIOINFORMATICS: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS, 2012, : 303 - 307
  • [4] Brainwave Classification for Acute Ischemic Stroke Group Level Using k-NN Technique
    Omar, Wan RosemehahWan
    Fuad, Norfaiza
    NasirTaib, Mohd
    Jailani, Rozita
    Isa, Roshakimah Mohd
    Mohamad, Zunuwanas
    Sharif, Zaiton
    PROCEEDINGS FIFTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS, MODELLING AND SIMULATION, 2014, : 117 - 120
  • [5] Determining of combustion process state based on flame images analysis using k-NN classification
    Sawicki, Daniel
    PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND HIGH ENERGY PHYSICS EXPERIMENTS 2017, 2017, 10445
  • [6] Resolving the Celestial Classification using Fine k-NN Classifier
    Yadav, Sangeeta
    Kaur, Amandeep
    Bhauryal, Neeraj Singh
    2016 FOURTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (PDGC), 2016, : 714 - 719
  • [7] Identification and Grading of Spasticity By Using AdaBoost and k-NN Techniques
    Albayrak, Yalcin
    Cetinel, Gokcen
    Gul, Sevda
    2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2017,
  • [8] K-NN DATA CLASSIFICATION TECHNIQUE USING SEMANTIC SEARCH ON ENCRYPTED RELATIONAL DATA BASE
    Uttarwar, Nikita
    Pradhan, M. A.
    2016 INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2016,
  • [9] Solving the Structure-Property Problem Using k-NN Classification
    Perevoznikov, Aleksandr
    Shestov, Alexey
    Permiakov, Evgenii
    Kumskov, Mikhail
    PATTERN RECOGNITION AND MACHINE INTELLIGENCE, 2011, 6744 : 49 - 53
  • [10] Fast k-NN classification using the cluster-space approach
    Jia, XP
    Richards, JA
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2005, 2 (02) : 225 - 228
12345