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Detection of melanoma cancer using gray level cooccurance matrix and artificial neural network methods

Author

Listed:
  • Nurjannah Syakrani

    (Department of Informatics, Politeknik Negeri Bandung, Bandung, Indonesia)

  • Rheza Ghivary Santoso

    (Department of Informatics, Politeknik Negeri Bandung, Bandung, Indonesia)

Abstract

Image feature extraction is a step of object extraction information in an image to recognize or distinguish it from other objects. The method used for feature extraction is the Gray Level Co-Occurance Matrix (GLCM). This research is related to the information of features calculation from the melanoma cancer and non-melanoma images using GLCM based on variations of gray level, which are 4, 8, 16, 32, and 64 as well as angles of GLCM orientation consisting of 4 and 8-way. The used features are angular second moment, contrast, correlation, entropy, inverse different moment and variance. Then, the feature values are used as input parameters to classify melanoma cancer by utilizing Artificial Neural Network (ANN). This experiment is conducted by using 45 data sets of images from www.skinvision.com. Generally, all of the experiment types results have accuracy of melanoma and non-melanoma classification by ANN more than 93%. Particularly, by inputting 6 parameters from GLCM feature extraction using (1) 4th degree of gray level and 4-way orientation angles, (2) 16th degree gray level and 8-way orientation angles and we can obtain the accuracy of ANN classification by 100%.

Suggested Citation

  • Nurjannah Syakrani & Rheza Ghivary Santoso, 2018. "Detection of melanoma cancer using gray level cooccurance matrix and artificial neural network methods," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(2), pages 91-101.
    • Handle: RePEc:apb:jaterr:2018:p:91-101
    DOI: 10.20474/jater-4.2.5
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    References listed on IDEAS

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    1. Eggi I. Putri & Rita Magdalena & Ledya Novamizanti, 2015. "The detection of cervical cancer disease using an adaptive thresholding method through digital image processing," Journal of Advances in Health and Medical Sciences, Balachandar S. Sayapathi, vol. 1(1), pages 30-36.
    2. Soraya Niha & Boonkanas Jantarasiriput & Narisara Tonyongdalaw & Navarat Vaichompu, 2016. "Reproductive Health Among Bangoebadae Muslim Women: Cervical Cancer Care," International Journal of Health and Medical Sciences, Mohammad A. H. Khan, vol. 2(3), pages 52-57.
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