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

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 sta...

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Veröffentlicht in:Computational intelligence and neuroscience Jg. 2021; H. 1; S. 9651957
Hauptverfasser: Huaping, Jia, Junlong, Zhao, Norouzzadeh Gil Molk, A. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: V. Rajinikanth
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2021/9651957