Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 9651957
• Main Authors: Huaping, Jia, Junlong, Zhao, Norouzzadeh Gil Molk, A. M.
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Cancer; Care and treatment; Cell cycle; Cell division; Diagnosis; Diagnosis, Computer-Assisted; Feature extraction; Feature selection; Field programmable gate arrays; Fuzzy logic; Heuristic methods; Humans; Image segmentation; Kernels; Mathematical optimization; Medical imaging equipment; Melanoma; Methods; Network management systems; Neural networks; Neural Networks, Computer; Noise control; Noise reduction; Optimization; Optimization algorithms; Simulation; Skin cancer; Skin Neoplasms - diagnosis; Support Vector Machine; Support vector machines; Germany
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/9651957

Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach’s higher superiority toward the others.