Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Detalles Bibliográficos
Autores: Ali, Mohamad Abou, Dornaika, Fadi, Arganda-Carreras, Ignacio, Ali, Hussein, Karaouni, Malak
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/364274
Acceso en línea:http://hdl.handle.net/10261/364274
Access Level:acceso abierto
Palabra clave:Convolutional neural net (CNN)
Vision transformer (ViT)
ImageNet models
Transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
Skin cancer
Naturalize
Segment Anything Model (SAM)
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oai_identifier_str oai:digital.csic.es:10261/364274
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network_name_str España
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dc.title.none.fl_str_mv Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
title Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
spellingShingle Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
Ali, Mohamad Abou
Convolutional neural net (CNN)
Vision transformer (ViT)
ImageNet models
Transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
Skin cancer
Naturalize
Segment Anything Model (SAM)
title_short Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
title_full Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
title_fullStr Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
title_full_unstemmed Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
title_sort Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
dc.creator.none.fl_str_mv Ali, Mohamad Abou
Dornaika, Fadi
Arganda-Carreras, Ignacio
Ali, Hussein
Karaouni, Malak
author Ali, Mohamad Abou
author_facet Ali, Mohamad Abou
Dornaika, Fadi
Arganda-Carreras, Ignacio
Ali, Hussein
Karaouni, Malak
author_role author
author2 Dornaika, Fadi
Arganda-Carreras, Ignacio
Ali, Hussein
Karaouni, Malak
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad del País Vasco
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Convolutional neural net (CNN)
Vision transformer (ViT)
ImageNet models
Transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
Skin cancer
Naturalize
Segment Anything Model (SAM)
topic Convolutional neural net (CNN)
Vision transformer (ViT)
ImageNet models
Transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
Skin cancer
Naturalize
Segment Anything Model (SAM)
description © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
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info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/364274
url http://hdl.handle.net/10261/364274
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language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126701OB-I00
https://doi.org/10.3390/biomedinformatics4010035
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep LearningAli, Mohamad AbouDornaika, FadiArganda-Carreras, IgnacioAli, HusseinKaraouni, MalakConvolutional neural net (CNN)Vision transformer (ViT)ImageNet modelsTransfer learning (TL)Machine learning (ML)Deep learning (DP)Skin cancerNaturalizeSegment Anything Model (SAM)© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class skin cancer classification, the research delves into the limitations of conventional diagnostic approaches, often hindered by subjectivity and resource constraints. The transformative potential of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms is underscored, emphasizing significant improvements in accuracy and accessibility. Methods: Utilizing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis is conducted, employing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering “Naturalize” augmentation technique is introduced. This technique leads to the creation of two indispensable datasets—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The “Naturalize” augmentation technique involves the segmentation of skin cancer images using the Segment Anything Model (SAM) and the systematic addition of segmented cancer images to a background image to generate new composite images. Results: The research showcases the pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer. The proficiency of AI in analyzing vast datasets and discerning subtle patterns significantly augments the diagnostic prowess of dermatologists. Quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations are meticulously evaluated. The culmination of these endeavors resulted in an unprecedented achievement—100% average accuracy, precision, recall, and F1-score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. Conclusion: This groundbreaking exploration highlights the transformative capabilities of AI-driven methodologies in reshaping the landscape of skin cancer diagnosis and patient care. The research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care. This pioneering work paves the way for a new era in dermatological diagnostics, heralding the dawn of unprecedented precision and efficacy in the identification and classification of skin cancers.This work is partially supported by grant GIU23/022 unded by the University of the Basque Country (UPV/EHU), and grant PID2021-126701OB-I00, funded by the Ministerio de Ciencia, Innovación y Universidades, AEI, MCIN/AEI/10.13039/501100011033, and by “ERDF A way of making Europe” (to I.A-C.)Peer reviewedMultidisciplinary Digital Publishing InstituteUniversidad del País VascoMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/364274reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126701OB-I00https://doi.org/10.3390/biomedinformatics4010035Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3642742026-05-22T06:33:51Z
score 15,811543

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