Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets

Malignant Melanoma is a dangerous form of skin cancer, and its detection is a challenging task as it appears in numerous ranges of size, shape, and shading with various skin tones. Also, artefacts like hairs, outlines, blood vessels, and boils add further complexity. A simplified clustering method i...

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Bibliographic Details
Published in:SN computer science 2023-05, Vol.4 (3), p.228, Article 228
Main Authors: Namburu, Anupama, Mohan, Senthilkumar, Chakkaravarthy, Sibi, Selvaraj, Prabha
Format: Article
Language:English
Subjects:
Advances in Applied Image Processing and Pattern Recognition
Algorithms
Automation
Blood vessels
Cancer
Centroids
Classification
Clustering
Complexity
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Deep learning
Dermatology
Fuzzy logic
Fuzzy sets
Information Systems and Communication Service
Melanoma
Methods
Morphology
Noise
Original Research
Pattern Recognition and Graphics
Segmentation
Skin cancer
Software Engineering/Programming and Operating Systems
Vision
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Summary:Malignant Melanoma is a dangerous form of skin cancer, and its detection is a challenging task as it appears in numerous ranges of size, shape, and shading with various skin tones. Also, artefacts like hairs, outlines, blood vessels, and boils add further complexity. A simplified clustering method is proposed in this paper to improve melanoma detection while reducing time complexity.The triangular membership function (TMF) is used to detect the initial regions for obtaining initial centroids. These initial centroids are used to apply intuitionistic fuzzy c-means clustering. The TMF helps in identifying the initial clusters and regions and reduces the number of iterations needed for segmentation. The proposed method effectively detects skin cancer regions with an average accuracy of 90% and performs well.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01701-8