An all-inclusive computer-aided melanoma diagnosis based on soft computing
Xiongfei Jiao
Department of Information Management, Hebei Children's Hospital, Shijiazhuang, Hebei, China
Search for more papers by this authorJuan Li
Department of Gynaecology, Xingtai Third Hospital, Xingtai, Hebei, China
Search for more papers by this authorCorresponding Author
Zhilei Zhao
Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai, Hebei, China
Correspondence
Zhilei Zhao, Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai 054000, Hebei, China.
Email: [email protected]
Benjamin Badami, University of Georgia, Athens, GA, USA.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Benjamin Badami
University of Georgia, Athens, Georgia, USA
Correspondence
Zhilei Zhao, Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai 054000, Hebei, China.
Email: [email protected]
Benjamin Badami, University of Georgia, Athens, GA, USA.
Email: [email protected]
Search for more papers by this authorXiongfei Jiao
Department of Information Management, Hebei Children's Hospital, Shijiazhuang, Hebei, China
Search for more papers by this authorJuan Li
Department of Gynaecology, Xingtai Third Hospital, Xingtai, Hebei, China
Search for more papers by this authorCorresponding Author
Zhilei Zhao
Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai, Hebei, China
Correspondence
Zhilei Zhao, Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai 054000, Hebei, China.
Email: [email protected]
Benjamin Badami, University of Georgia, Athens, GA, USA.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Benjamin Badami
University of Georgia, Athens, Georgia, USA
Correspondence
Zhilei Zhao, Information Center, The Second Affiliated Hospital of Xingtai Medical College, Xingtai 054000, Hebei, China.
Email: [email protected]
Benjamin Badami, University of Georgia, Athens, GA, USA.
Email: [email protected]
Search for more papers by this authorAbstract
This paper provides a different optimized approach for melanoma diagnosis from the inputted dermoscopy images. The technique is a pipeline technique with four main steps, including noise reduction, lesion segmentation, feature selection, and final classification. For decreasing the complexity of the feature extraction stage, Fuzzy C-means has been used. The classifier has been improved based on a developed decision tree. The modification of the classifier is based on a new enhanced design of a metaheuristic, called Quantum Fluid Search Optimizer. The efficiency of the suggested technique is calculated by considering some measurement indicators and their achievements are compared with five other latest methods. The results showed the maximum accuracy equal to 94.12% with the highest precision being achieved by the proposed method. The results also indicate that the proposed method with the highest value of 91.18% sensitivity against the other techniques, provides the highest reliability.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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