Abstract
The main objective of this paper is to classify the skin diseases using image classification methods. For examining the texture of the image, a statistical method gray-level co-occurrence matrix (GLCM) was used. GLCM considers the spatial relationship of pixels and characterizes texture of an image by calculating how often pairs of the pixel with specific values and specified spatial relationship occur in an image. The presented work here is focused on the extraction of GLCM features inclusive of contrast, correlation, homogeneity, and energy. Fuzzy c-means clustering along with GLCM is proposed for reducing the time taken for skin disease classification. With simulation results, it is shown that the proposed method is more efficient than GLCM alone method.
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Mullangi, P., Srinivasa Rao, Y., Kotipalli, P. (2018). Texture and Clustering-based Skin Disease Classification. In: Urooj, S., Virmani, J. (eds) Sensors and Image Processing. Advances in Intelligent Systems and Computing, vol 651. Springer, Singapore. https://doi.org/10.1007/978-981-10-6614-6_11
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DOI: https://doi.org/10.1007/978-981-10-6614-6_11
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