Anfis to Detect Brain Tumor Using MRI

  • Authors

    • Susmita Mishra
    • M Prakash
    • A Hafsa
    • G Anchana
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17763
  • Brain tumour, magnetic reasonance imaging(MRI), image preprocessing, adaptive histogram equalisation(AHE), adaptive neural network(ANN), fuzzy logic.

  • Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage.

     

  • References

    1. [1] Bhaiya LP & Goswami MS, “Classification of MRI brain images using neuro fuzzy modelâ€, International Journal of Engineering Inventions, Vol.1, No.4,(2012), pp.27-31.

      [2] Sushil Narang, Applying Fuzzy Logic to Image Processing Applications. http://www.cbsmohali.org/img/nine.pdf

      [3] Goel R, Kumar V, Srivastava S & Sinha AK, “A Review of Feature Extraction Techniques for Image Analysisâ€, International Journal of Advanced Research in Computer and Communication Engineering, Vol.6,No.2,(2017), pp.153-155.

      [4] Anjna EA & Er RK, “Review of Image Segmentation Techniqueâ€, International Journal of Advanced Research in Computer Science, Vol.8, No.4,(2017).

      [5] Revathi C & Jagadeesh B, “Segmentation of Brain Images for Tumor Detection using Neuro-Fuzzy Inference Systemâ€, International Journal of Current Engineering and Scientific Research (IJCESR), Vol.4, No.12,(2017).

      [6] Parra CA & Iftekharuddin K, “Automated brain data segmentation and Pattern recognition using ANNâ€, Proceedings of CIRAS, (2003).

      [7] Chavan NV, Jadhav BD & Patil PM, “Detection and Classification of Brain Tumorsâ€, International Journal of Computer Applications, Vol.112, No.8, (2015).

      [8] Murugesan M & Sukanesh R, “Automated Detection of Brain Tumor in EEG Signals using Artificial Neural Networks†, IEEE Conference on Advances in Computing, Control, & Telecommunication Technologies, (2009).

      [9] Aware MV, Kothari AG & Choube SO, “Application of adaptive neuro-fuzzy controller(Anfis) for brain tumor detection using voltage source inverterâ€, Power Electronics and Motion Control Conference, (2000).

      [10] Nelakuditi, U. R. (2018). Introduction. International Journal of Biomedical Engineering and Technology, 26(3–4).

      [11] Surendar, A. (2018, January 1). Letter from the desk of editor’s. International Journal of Pharmaceutical Research, 10(1).

      [12] Kharat KD, Kulkarni PP & Nagori MB, “Brain Tumor Classification Using Neural Network Bayes Methodsâ€, International Journal of Computer Applications, Vol.112, (2015).

      [13] Clark MC & Hall LO, “Automatic tumor segmentation using Knowledge based techniquesâ€, IEEE transactions on medical imaging, Vol.17, No.2, (1998).

      [14] Deshmukh RJ & Khule RS, “Brain Tumor Detection using Artificial Neural Network Fuzzy Inference System(ANFIS)â€, International Journal of Computer Applications Technology and Research, Vol.3,No.3, (2014).

      [15] Roy S, Sadhu S, Bandyopadhyay SK, Bhattacharyya D & Kim TH, “Brain tumor classification using adaptive neuro-fuzzy inference system from MRIâ€, International Journal of Bio-Science and Bio-Technology, Vol.8, No.3,(2016), pp.203-218.

      [16] Nalbalwar R, Majhi U, Patil R & Gonge S, “Detection of Brain Tumor using ANNâ€, International Journal of Research in Advent Technology, Vol.2, No.4, (2014).

      [17] Rani N & Vashisth S, “Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Networkâ€, International Journal of Computer Applications, Vol.146, No.12, (2016).

      [18] Ramaraju PV & Baji S, “Brain Tumor Detection, Classification, Detection and Segmentation Using Digital Image Processing and Probabilistic Neural Network Techniquesâ€, International Journal of Emerging Trends in Electrical and Electronics, Vol.10, No.10, (2014).

      [19] Nithin Krishna J, Mohammad Aslam J, Satya C, Narayanan D & Padma Priya K, “Tumor Detection using Fuzzy Logic and GMRFâ€, International Journal of Advanced Computational Engineering and Networking, Vol.3, No.5, (2015).

      [20] G Mussabekova, S Chakanova, A Boranbayeva, A Utebayeva, K Kazybaeva, K Alshynbaev (2018). Structural conceptual model of forming readiness for innovative activity of future teachers in general education school. Opción, Año 33. 217-240

      [21] G Cely Galindo (2017) Del Prometeo griego al de la era-biós de la tecnociencia. Reflexiones bioéticas Opción, Año 33, No. 82 (2017):114-133

  • Downloads

  • How to Cite

    Mishra, S., Prakash, M., Hafsa, A., & Anchana, G. (2018). Anfis to Detect Brain Tumor Using MRI. International Journal of Engineering & Technology, 7(3.27), 209-214. https://doi.org/10.14419/ijet.v7i3.27.17763