Computer Aided Diagnosis of Skin Tumours from Dermal Images.

Thamizhvani TR, Lakshmanan S, Rajaraman S
Hemanth D., Smys S. (eds) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham


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