Human skin ringworm detection using wavelet and curvelet transforms: a comparative study
by Manas Saha; Mrinal Kanti Naskar; B.N. Chatterji
International Journal of Computational Vision and Robotics (IJCVR), Vol. 11, No. 3, 2021

Abstract: The common human skin disease called ringworm is investigated in the light of computer vision. Two distinct methodologies are developed for its detection. The first methodology implements three-level multi-wavelet decomposition of the skin images and subsequent evaluation of the approximation and detail subband energies which act as the texture characterising features. The second methodology incorporates the curvelet to segment the circular protrusion of the skin images especially with ringworms followed by statistical texture investigation by grey-level co-occurrence matrix (GLCM). After feature extraction by both the methodologies, binary classifier called the support vector machine (SVM) recognises the images as ringworm with detection accuracy of around 87% and 80% for the first and second methodologies respectively. In addition, the performance indexing parameters of SVM classification like sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) which are not previously addressed are evaluated. Both the methodologies are comprehensively demonstrated and compared to select the better one. The selected method is then compared with the available technique and commented upon.

Online publication date: Fri, 21-May-2021

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