Intelligent Approach for Skin Tumor Finding and Segmentation via Bivariate Shrinkage and Speckle Reducing Anisotropic Diffusion

Abstract

Any irregular tissue development is called skin tumors. Malignant Melanoma and Basal Cell Carcinoma are among the most common forms of skin tumors. For the grouping skin lesions, the description and computation of various attributes like precise location, degree, form, and form is important. Advances of medical imaging technologies make diagnostic imaging a critical resource in medicine. For the clinical setting purpose magnetic resonance imaging, computed tomography, magnetic resonance angiography, and X-Ray angiography is frequently utilized. Computerized diagnostic execution is required due to the higher volume of clinical images and the recent developments in computing technologies that have rendered it possible to build these systems. For this research, we used the multivariate regression shrinkage added to the actual and complex noise reduction coefficients. The Speckle Reducing Anisotropic Diffusion (SRAD) helps to eliminate the noise after the reverse transition has actually occurred further to reduce the noise. This proposed system has been validated by various parameters like RMSE (Root Mean Square Error), PSNR (peak signal to Noise Ratio), PRD (Percent Residual Difference), (SSI) Structure Similarity Index and CCR (Correlation Coefficient are Related) to DTCW (Dual-Tree Complex Wavelet), CT (Curvelet Transform) and the WT (Wavelet Transform).