Feature Extraction from Dermoscopy Images for an Effective Diagnosis of Melanoma Skin Cancer

Abstract

The aim of this paper is to extract some distinct geometric features from dermoscopy images to classify benign and malignant melanomas. To avoid skin biopsy which is an invasive technique, diagnosis of melanoma skin cancer from dermoscopy images was developed. It is a very challenging task due to some reasons. Firstly, high degree of intraclass variation exists among melanoma images while low interclass variation is found between melanoma and non-melanoma images. Secondly, benign and malignant melanoma images are visually similar to a great extent. And finally noises like hair are always present in skin images which make difficult to analyse the images. In this work, we used fundamental ABCD rule to detect malignant melanoma and benign lesion based on quantitative measures. In our proposed technique, we extracted a new feature which is the difference between maximum and minimum Feret diameters of the best fit ellipse to skin lesion. This discriminative feature alone classified the melanomas with 86.5% accuracy. In our approach, we applied the feature extraction block containing all parameters to 200 images and the overall accuracy of 98% was achieved to detect malignant and benign melanoma from the images. A Back-propagation Neural Network (BNN) model was developed and eventually used as a classifier in this proposed method.