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Computer-aided Diagnosis of Melanoma Using Border
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Abstract this paper presents a new computer aided diagnosis system for malignant melanoma. The novelty lies in the optimization of selection and set success due to texture, borderbased and geometric properties of melanoma lesions. From the use of wavelet decomposition, the texture features are derived. From the construction of boundary series, the boundary features and the analysis space model are derived. The corresponding frequency domain and geometric features from the shape index are given. The optimized selection of functions through the use of gain rate method is shown to be effective in the application of melanoma diagnosis. Classification is done by using four classifiers: support vector machine, random forest, logistic model tree and hidden naive Bayes. The system is used to segment a set of 289 dermoscopic images (114 malignant, 175 benign) into train, validation and test image sets. The system achieves 91.26% and AUC value and accuracy of 0.937 when using 23 functions. Other important findings include (I) clear advantages in obtaining complementary texture information compared to boundary and geometric features, and (II) making a greater contribution to the feature set than boundary based feature optimization.
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Skin cancer diagonis using abcd rule
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This research present asymmetry, border irregularity, color variation, and diameter (ABCD) feature extraction of image dermatoscopic for melanoma skin cancer diagnosis. ABCD feature is the important information based on morphology analysis of image dermatoscopic lesion. ABCD feature is used to calculate Total Dermatoscopic Value (TDV) for melanoma skin cancer diagnosis. Asymmetry feature consist information of asymmetry and lengthening index of the lesion. Border irregularity feature consist information of compactness index, fractal dimension, edge abruptness, and pigmentation transition from the lesion. Color homogeneity feature consist information of color homogeneity and the correlation between photometry and geometry of the lesion. Diameter extraction is diameter of the lesion. There are three diagnosis that is used on this research i.e. melanoma, suspicious, and benign skin lesion. The experiment uses 30 samples of image dermatoscopic lesion that is s
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