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Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology

This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, Calif...

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Autores principales: Riazi Esfahani, Parsa, Mazboudi, Pasha, Reddy, Akshay J, Farasat, Victoria P, Guirgus, Monica E, Tak, Nathaniel, Min, Mildred, Arakji, Gordon H, Patel, Rakesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518209/
https://www.ncbi.nlm.nih.gov/pubmed/37750114
http://dx.doi.org/10.7759/cureus.44120
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author Riazi Esfahani, Parsa
Mazboudi, Pasha
Reddy, Akshay J
Farasat, Victoria P
Guirgus, Monica E
Tak, Nathaniel
Min, Mildred
Arakji, Gordon H
Patel, Rakesh
author_facet Riazi Esfahani, Parsa
Mazboudi, Pasha
Reddy, Akshay J
Farasat, Victoria P
Guirgus, Monica E
Tak, Nathaniel
Min, Mildred
Arakji, Gordon H
Patel, Rakesh
author_sort Riazi Esfahani, Parsa
collection PubMed
description This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.
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spelling pubmed-105182092023-09-25 Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology Riazi Esfahani, Parsa Mazboudi, Pasha Reddy, Akshay J Farasat, Victoria P Guirgus, Monica E Tak, Nathaniel Min, Mildred Arakji, Gordon H Patel, Rakesh Cureus Dermatology This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma. Cureus 2023-08-25 /pmc/articles/PMC10518209/ /pubmed/37750114 http://dx.doi.org/10.7759/cureus.44120 Text en Copyright © 2023, Riazi Esfahani et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Dermatology
Riazi Esfahani, Parsa
Mazboudi, Pasha
Reddy, Akshay J
Farasat, Victoria P
Guirgus, Monica E
Tak, Nathaniel
Min, Mildred
Arakji, Gordon H
Patel, Rakesh
Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title_full Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title_fullStr Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title_full_unstemmed Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title_short Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology
title_sort leveraging machine learning for accurate detection and diagnosis of melanoma and nevi: an interdisciplinary study in dermatology
topic Dermatology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518209/
https://www.ncbi.nlm.nih.gov/pubmed/37750114
http://dx.doi.org/10.7759/cureus.44120
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