RETRACTED: Optimal skin cancer detection by a combined ENN and Fractional Order Coot Optimization Algorithm

Abstract

In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. There are different types of skin cancer that can be diagnosed early. Providing a method that facilitates the diagnosis of this cancer in the early stages is very useful and valuable. This study proposes a new algorithm to efficient diagnosis of the skin cancer by proper extracting and selection of the features from the image (gradient, texture, and geometric features) and classifying these features to diagnose the cancerous cases. The classification step of the methodology based on Elman neural network. Due to the effectiveness of the metaheuristic algorithms in optimal solving the problems in the last decade, using these kinds of optimization techniques is useful for the diagnosis purpose. Therefore, both parts of feature selection and classification stages are optimized by an improved metaheuristic algorithm, called Fractional Order Coot Optimization Algorithm (FO-COA) which is designed in this study. Final designed model is then performed to the SIIM-ISIC Melanoma dataset and its achievements have been validated with several newest approaches to show the method higher efficiency.