Total views : 358

Detection and classification of lung cancer MRI images by using enhanced k nearest neighbor algorithm

Affiliations

  • Department of Computer Science, Bishop Heber College, Tiruchirappalli - 620017, Tamil Nadu, India

Abstract


Objectives: To detect and classify the malignant cancer tissues and benign cancer tissues in MR lung cancer images by using k nearest neighbor mining algorithm. Methods/Statistical Analysis: In this paper, the Enhanced K Nearest Neighbor (EKNN) algorithm is executed to identify the lung cancer images. The k nearest neighbor technique is an important method of data mining algorithms. Findings: This work implicates four stages such as pre-processing, feature extraction, classification and detection of cancer tissues. In preprocessing stage, morphological process is used to filter the irrelevant noisy data in images. In the second phase, statistical and discriminator algorithm is used to extract the images. In the last stage, the improved k Nearest Neighbor (EKNN) method has been used to classify and identify the cancerous tissues in MRI images. The detection of cancer tissues and classification is done by executing four steps of Enhanced k Nearest Neighbor method which are measuring the Euclidean distance value, determining the k value, calculating the minimum distance and detecting the cancerous cells. Improvements/Applications: The experimental study with enhanced k nearest neighbor method shows better and promising classification result for classifying benign and malignant tissues.

Keywords

Geometrical and Statistical Properties, Image Classification, Image Mining, MRI Images, k Nearest Neighbor, Morphological Method.

Full Text:

 |  (PDF views: 345)

References


  • Zhang X, Hao S, Xu C, Qian X, Wang M, Jiang J. Image classification based on low-matrix recovery and naïve bayes collaborative representation. Neurocomputing. 2015; 169(2):110–18.
  • Gajjar TY, Chauhan NC. A review on image mining frameworks and algorithms. International Journal of Computer Science and Information Technologies. 2012; 3(3):4064–6.
  • Lei B, Tan EL, Chen S, Dong N, Wang T. Saliency driven image classification method based on histogram mining and image score. Pattern Recognition. 2015; 48(8):2567–80.
  • Zhu X, Xie Q, Zhu Y, Liu X, Zhang S. Multi sparsity kernel reconstruction for multi class image classification. Neurocomputing. 2015; 169(2):43–9.
  • Hong R, Pan J, Hao S, Wang M, Xuo F, Wu X. Image quality assessment based on matching pursuit. Information Science. 2014; 273:196–211.
  • Hong R, Tang J, Tan HK, Ngo CW, Yan S, Chua TS. Beyond searching: Event driven summarization for web videos. ACM Transactions on Multimedia Computing, Communications, and Applications. 2011; 7(4).
  • Hong R, Wang M, GaoY, Tao D, Li X, Wu X. Image annotation by multiple learning with discriminative feature mapping selection. IEEE Transactions on Cybernetics. 2014; 44(5):669–80.
  • Wu X, Kumar P, Quinlan JR, Ghosh J, Yang Q, Motoda H. Top 10 algorithm in data mining. Knowledge and Information Systems. 2008; 14(1):1–37.
  • Thamilselvan P, Sathiaseelan JGR. A comparative study of data mining algorithms for image classification. International Journal of Education and Management Engineering. 2015; 5(2):1–9.
  • Mangai JA, Wagle S, Kumar VS. An improved k nearest neighbor classifier using interestingness measures for medical image mining. International Journal of Medical Health Biomedical Bioengineering and Pharmaceutical Engineering. 2013; 7(9):236–40.
  • Chen CK. The classification of cancer stage in micro array data. Computer Method and Programs in Biomedicine. 2012; 108(3):1070–7.
  • Chen HI, Miser J, Kuan CF, Fang YA, Lam C, Li YC. Critical laboratory result reporting systems in cancer patients. Computer Methods and Programs in Biomedicine. 2013; 111(1):249–54.
  • Lee MY, Yang CS. Entropy based feature extraction and decision tree induction for breast cancer diagnosis with standardize thermograph images. Computer Methods and Programs in Biomedicine. 2010; 100(3):269–82.
  • Su S Q, Zhang C, Huang G, Zhu Y. An intelligent decision support method for diagnosis of colorectal cancer through serum tumor markers, Computer methods and programs in biomedicine, 2010,100(2), 100(2), pp.97-107.
  • Kalinli A, Sarikoc F, Akgun H, Ozturk F. Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples. Computer Methods and Programs in Biomedicine. 2013; 110(3):298–307.
  • Sartakhti, Zanooei MH, Mozafari K. Hepatitis disease diagnosis using a novel hybrid method based on support vectormachine and simulated annealing. Computer methods and programs in biomedicine. 2012; 108(2):570–9.
  • Amini S, Homayouni S, Safari A. Semi supervised classification of hyperspectral image using random forest method. IEEE International Geoscience and Remote Sensing Symposium; 2014. p. 2866–9.
  • Ruta A, Li Y. Learning pairwise image similarities for multi classification using kernel regression trees. Pattern Recognition. 2012; 45(4):1396–408.
  • Bhuvaneswari C, Aruna P, Loganathan D. A new fusion model for classification of the lung disease using generic algorithm. Egyptian Informatics Journal. 2014; 15(2):69–77.
  • Yin H, Cao Y, Sun H. Combining pyramid representation and adaboost for urban scene classification using high-resolution synthetic aperture radar images. IET Radar Sonar Navigation. 2011; 5(1):58–64.
  • Lin GC, wang WJ, wang CM, sun SY. Automated classification of multi spectral MR images using linear discriminant analysis. Computerized Medical Imaging and Graphics. 2010; 34(4):251–68.
  • Mala K, Sadasivam V, Alagappan S. Neural network based texture analysis of computed tomography images for fatty and cirrhosis liver classification. Applied Soft Computing. 2015; 32:80–6.
  • Zhang X, Hu B, Ma X, Xu L. Resting state whole brain functional connectivity networks for MCI classification using L2 regularized logistic regression. IEEE Transactions on Nano Bioscience. 2015; 14(2):237–47.
  • Davatzikos C, Fan Y, Shen D, Rensick SM. Detection of prodromal Alzheimer’s disease via pattern classification of MRI imaging. Neurobiological Aging. 2008; 29(4):514–23.
  • Sreedhar K, Panlal B. Enhancement of images using morphological transformations. International Journal of Computer Science and Information Technology. 2012; 4(1):33–50.
  • Ali AH, Hadi EM, Mazhir SN. Diagnosis of liver from computed tomography images using unsupervised classification with geometrical and statistical features. International Journal of Advanced Research in Computer Science and Software Engineering. 2015; 5(3):28–38.
  • Patil SA, Kuchanur MB. Lung cancer image classification using image processing. International Journal of Engineering and Innovative Technology. 2012; 2(3):37–42.
  • Ramteke RJ, Monali YK. Automatic medical image classification and abnormality detection using k nearest neighbor. International Journal of Advanced Computer Research. 2012; 2(4):190–6.
  • Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques. Indian Journal of Science and Technology. 2016; 9(1):1–12.
  • Kumar NS, Arun M. Enhanced classification algorithms for the satellite image processing. Indian Journal of Science and Technology. 2015; 8(15):1–9.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.