RETRACTED ARTICLE: Semantic segmentation-based skin cancer detection

Melanoma detection requires dermoscopy image segmentation, one of the medical image segmentation domains. Melanoma, the most dangerous form of skin cancer, can strike without warning on healthy skin or grow from a pre-existing lesion. Skin lesion border segmentation is required to find lesion locati...

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Published in:Soft computing (Berlin, Germany) Germany), 2023-08, Vol.27 (16), p.11895-11903
Main Author: Renuka, N.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Blurring
Cancer
Cell division
Computational Intelligence
Control
Datasets
Deep learning
Engineering
Focus
Image segmentation
Lesions
Mathematical Logic and Foundations
Mechatronics
Medical imaging
Robotics
Semantic segmentation
Skin cancer
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description Melanoma detection requires dermoscopy image segmentation, one of the medical image segmentation domains. Melanoma, the most dangerous form of skin cancer, can strike without warning on healthy skin or grow from a pre-existing lesion. Skin lesion border segmentation is required to find lesion locations in histopathologic imaging effectively. True, accurate skin lesion segmentation remains challenging due to blurring boundaries, necessitating an accurate and automated skin lesion segmentation approach. Semantic segmentation using deep learning architecture has been effectively employed in this paper for the desired task. The segmentation of the skin lesson is performed using u-net-based deep learning model and analyzed using standard and valid evaluation metrics. The simulation is carried out on the available dataset and implemented on the Python Collab® tool.
doi_str_mv 10.1007/s00500-023-08557-3
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subjects Artificial Intelligence
Blurring
Cancer
Cell division
Computational Intelligence
Control
Datasets
Deep learning
Engineering
Focus
Image segmentation
Lesions
Mathematical Logic and Foundations
Mechatronics
Medical imaging
Robotics
Semantic segmentation
Skin cancer
title RETRACTED ARTICLE: Semantic segmentation-based skin cancer detection
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