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Computer Aided Diagnosis of Skin Tumours from Dermal Images.
Skin tumour is uncontrolled growth of skin cells which may be cancerous. The aim is to develop computer aided diagnosis for skin tumours. The dermal images of three types such as benign tumour, malignant melanoma and normal moles obtained from the authorised PH2 database. Pre-processing performed to remove hair cells. Contour based level set technique for segmentation of the lesion from which clinical and morphological features are extracted. The signiﬁcant features are obtained using Random Subset Feature Selection technique. Classiﬁcation is performed using three classiﬁers such as back propagation, pattern recognition and support vector machine. Classiﬁer Efﬁciency of three classiﬁers is determined to be 94, 96 and 98% respectively with the Classiﬁer performance parameters. One way ANOVA test is performed to analyse the efﬁciency of the three classiﬁers. With these results, Support vector machine is conﬁgured as accurate classiﬁer for classiﬁcation. For supporting codes and data see: GitHub1 GitHub2 GitHub3 GitHub4