Fusion Of Structural And Textural Features For Melanoma Recognition

Abstract

Melanoma is the deadliest form of skin cancer. Melanoma is very common now-a-days because of the meteoric rise in global warming leading to ozone layer depletion. Melanoma is extremely fatal in nature. Because of this early detection of this disease can affect the result of the illness and improve the chance of surviving. A great improvement of deep learning algorithms in image recognition tasks has led to great success for medical image analysis, especially melanoma classification for skin  cancer diagnosis. Image recognition for medical image analysis is done via activation functions in a deep neural network. In this paper, we show that a deep neural network model with adaptive piecewise linear units can achieve excellent results in melanoma recognition. According to the results of several experiments, a convolutional deep neural network outperforms the same network with different activation functions in the melanoma classification task. All experiments are carried out considering the data given in the  International Skin Imaging Collaboration (ISIC) 2018 Skin Lesion Analysis towards Melanoma Detection.