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Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning

Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working princ...

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Autores principales: Cullell-Dalmau, Marta, Noé, Sergio, Otero-Viñas, Marta, Meić, Ivan, Manzo, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969634/
https://www.ncbi.nlm.nih.gov/pubmed/33748163
http://dx.doi.org/10.3389/fmed.2021.644327
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author Cullell-Dalmau, Marta
Noé, Sergio
Otero-Viñas, Marta
Meić, Ivan
Manzo, Carlo
author_facet Cullell-Dalmau, Marta
Noé, Sergio
Otero-Viñas, Marta
Meić, Ivan
Manzo, Carlo
author_sort Cullell-Dalmau, Marta
collection PubMed
description Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working principles of these algorithms. In our opinion, getting hands-on experience with these tools through a simplified but accurate model can facilitate their understanding in an intuitive way. The visualization of the results of the operations performed by deep learning algorithms on dermatological images can help students to grasp concepts like convolution, even without an advanced mathematical background. In addition, the possibility to tune hyperparameters and even to tweak computer code further empower the reach of an intuitive comprehension of these processes, without requiring advanced computational and theoretical skills. This is nowadays possible thanks to recent advances that have helped to lower technical and technological barriers associated with the use of these tools, making them accessible to a broader community. Therefore, we propose a hands-on pedagogical activity that dissects the procedures to train a convolutional neural network on a dataset containing images of skin lesions associated with different skin cancer categories. The activity is available open-source and its execution does not require the installation of software. We further provide a step-by-step description of the algorithm and of its functions, following the development of the building blocks of the computer code, guiding the reader through the execution of a realistic example, including the visualization and the evaluation of the results.
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spelling pubmed-79696342021-03-19 Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning Cullell-Dalmau, Marta Noé, Sergio Otero-Viñas, Marta Meić, Ivan Manzo, Carlo Front Med (Lausanne) Medicine Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working principles of these algorithms. In our opinion, getting hands-on experience with these tools through a simplified but accurate model can facilitate their understanding in an intuitive way. The visualization of the results of the operations performed by deep learning algorithms on dermatological images can help students to grasp concepts like convolution, even without an advanced mathematical background. In addition, the possibility to tune hyperparameters and even to tweak computer code further empower the reach of an intuitive comprehension of these processes, without requiring advanced computational and theoretical skills. This is nowadays possible thanks to recent advances that have helped to lower technical and technological barriers associated with the use of these tools, making them accessible to a broader community. Therefore, we propose a hands-on pedagogical activity that dissects the procedures to train a convolutional neural network on a dataset containing images of skin lesions associated with different skin cancer categories. The activity is available open-source and its execution does not require the installation of software. We further provide a step-by-step description of the algorithm and of its functions, following the development of the building blocks of the computer code, guiding the reader through the execution of a realistic example, including the visualization and the evaluation of the results. Frontiers Media S.A. 2021-03-04 /pmc/articles/PMC7969634/ /pubmed/33748163 http://dx.doi.org/10.3389/fmed.2021.644327 Text en Copyright © 2021 Cullell-Dalmau, Noé, Otero-Viñas, Meić and Manzo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Cullell-Dalmau, Marta
Noé, Sergio
Otero-Viñas, Marta
Meić, Ivan
Manzo, Carlo
Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title_full Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title_fullStr Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title_full_unstemmed Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title_short Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning
title_sort convolutional neural network for skin lesion classification: understanding the fundamentals through hands-on learning
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969634/
https://www.ncbi.nlm.nih.gov/pubmed/33748163
http://dx.doi.org/10.3389/fmed.2021.644327
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