Skin cancer detection and stage prediction using image processing techniques

  • Authors

    • Sheeju Diana christ university, bengaluru
    • Ramamurthy B christ university, bengaluru
    2018-02-27
    https://doi.org/10.14419/ijet.v7i1.8643
  • Back Propagation, Feature Extraction, Histogram Equalization, Melanoma, Neural Network, Non-Melanoma, Noise Removal, Segmentation.

  • Abstract

    Skin cancer is one of the perilous forms of cancer that most recently occurred in preceding and in recent years as well. Early detection of skin cancer is curable and it eliminates the cost that is spent on the advanced treatment. Skin cancer mainly occurs due to exposure to sun’s ultraviolet radiation and other environmental threats. It can be categorized into, Melanoma and Non-Melanoma. Melanoma is dangerous one. Once it is occurred it starts spreading across other parts of the body if not treated in the early stages. Non-Melanoma is a static cancer which does not affect the normal cells of the skin. This paper aims to develop an application to detect skin cancer and stage prediction using Image Processing Techniques. Stage is predicted, so that the treatment for the same is done without any delay. Skin cancer affected image is taken as input and various preprocessing techniques is applied for the same. The Preprocessing Techniques such as Noise Removal is applied on the image to filter out the noise. Filtered image is enhanced using Histogram Equalization and image is segmented to extract the affected portion. The Area, Perimeter and Eccentricity values are calculated for the affected portion of the skin. The values are then fed into the Neural Networks using Back Propagation algorithm in order to predict the Stage and type of the Skin cancer.

  • References

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  • How to Cite

    Diana, S., & B, R. (2018). Skin cancer detection and stage prediction using image processing techniques. International Journal of Engineering & Technology, 7(1), 204-209. https://doi.org/10.14419/ijet.v7i1.8643

    Received date: 2017-11-11

    Accepted date: 2018-01-30

    Published date: 2018-02-27