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
  • [31] Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis
    Awty-Carroll, Danny
    Clifton-Brown, John
    Robson, Paul
    PLANT METHODS, 2018, 14
  • [32] Detecting Diabetic Retinopathy in Fundus Images using Combined Enhanced Green and Value Planes (CEGVP) with k-NN
    Hardas, Minal
    Mathur, Sumit
    Bhaskar, Anand
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (01) : 260 - 268
  • [33] Detection of NS1 from SERS Spectra Using K-NN Integrated with PCA
    Othman, N. H.
    Lee, Khuan Y.
    Radzol, A. R. M.
    Mansor, W.
    Rashid, U. R. M.
    2016 IEEE EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES), 2016, : 91 - 95
  • [34] Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM
    Dargan, Shaveta
    Kumar, Munish
    Garg, Anupam
    Thakur, Kutub
    SOFT COMPUTING, 2020, 24 (13) : 10111 - 10122
  • [35] Tomato classification according to organoleptic maturity (coloration) using machine learning algorithms K-NN, MLP, and K-Means Clustering
    Nino Pacheco, Wolffang D.
    Jimenez Lopez, Fabian R.
    2019 XXII SYMPOSIUM ON IMAGE, SIGNAL PROCESSING AND ARTIFICIAL VISION (STSIVA), 2019,
  • [36] Tumor Detection and Classification using Decision Tree in Brain MRI
    Naik, Janki
    Patel, Sagar
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2014, 14 (06): : 87 - 91
  • [37] A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification
    Anton Akusok
    Yoan Miche
    Jozsef Hegedus
    Rui Nian
    Amaury Lendasse
    Cognitive Computation, 2014, 6 : 432 - 445
  • [38] A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification
    Akusok, Anton
    Miche, Yoan
    Hegedus, Jozsef
    Nian, Rui
    Lendasse, Amaury
    COGNITIVE COMPUTATION, 2014, 6 (03) : 432 - 445
  • [39] A Benchmarking: Feature Extraction and Classification of Agricultural Textures Using LBP, GLCM, RBO, Neural Networks, k-NN, and Random Forest
    Aygun, Sercan
    Gunes, Ece Olcay
    2017 6TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS, 2017, : 11 - 14
  • [40] IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences
    Kumar, Rahul
    Gupta, Ankur
    Arora, Harkirat Singh
    Raman, Balasubramanian
    ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 2022, 22 (01)
12345