2023
DOI: 10.3389/fpubh.2023.1123581
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A novel approach toward skin cancer classification through fused deep features and neutrosophic environment

Abstract: Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, … Show more

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Cited by 8 publications
(2 citation statements)
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“…By engaging single-valued trapezoidal neutrosophic numbers, the study underscores the adaptability and resilience of neutrosophic sets in scenarios involving multi-attribute group decision-making. Furthermore, the authors in [20] established an innovative approach for skin cancer classification, utilizing fused deep features within a neutrosophic framework. This study shows how neutrosophic sets enhance accuracy and reliability in medical diagnostics, showcasing their adaptability in this environment.…”
Section: Related Workmentioning
confidence: 99%
“…By engaging single-valued trapezoidal neutrosophic numbers, the study underscores the adaptability and resilience of neutrosophic sets in scenarios involving multi-attribute group decision-making. Furthermore, the authors in [20] established an innovative approach for skin cancer classification, utilizing fused deep features within a neutrosophic framework. This study shows how neutrosophic sets enhance accuracy and reliability in medical diagnostics, showcasing their adaptability in this environment.…”
Section: Related Workmentioning
confidence: 99%
“…Artificial intelligence (AI) techniques has shown great promise in automating the diagnosis process. These algorithms extract deep features from dermatoscopy images and classify them accurately, helping dermatologists make better-informed decisions [ 9 ]. Identifying the type of skin lesion indeed is challenging, especially in the early stages, due to the similarity of their characteristics.…”
Section: Introductionmentioning
confidence: 99%