Dermoscopic image segmentation and classification using machine learning algorithms

Dermoscopy is the method of examining the skin lesions. It is especially used for diagnosing melanoma, a type of skin cancer. Image segmentation and classification are important tools to provide the information about the Dermoscopic images clinically in terms of its size and shape. Many algorithms were developed for classification and segmentation of Dermoscopic images. This work proposes the tasks of extracting, classifying and segmenting the Dermoscopic image using the machine learning algorithms. The algorithms such as Back Propagation network (BPN), Radial Basis Function Network (RBF) and Extreme Learning Machine (ELM) are used. The features are extracted from the Dermoscopic image and these features are used to train the classifiers. The trained networks are used for segmentation. The results are compared with the ground truth images and their performance is evaluated. The results proved that the ELM has better accuracy, faster training period and it provides better segmentation than the BPN and RBF neural networks.