An all‐inclusive computer‐aided melanoma diagnosis based on soft computing

• Published in: International journal of imaging systems and technology Vol. 32; no. 4; pp. 1294 - 1306
• Main Authors: Jiao, Xiongfei, Li, Juan, Zhao, Zhilei, Badami, Benjamin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2022; Wiley Subscription Services, Inc
• Subjects: Classifiers; Decision trees; Design modifications; Diagnosis; Feature extraction; feature selection; fuzzy C‐means; Heuristic methods; Image segmentation; melanoma; quantum fluid search optimization algorithm; Soft computing
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22708

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.