Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783666265790873600 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7969634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT cullelldalmaumarta convolutionalneuralnetworkforskinlesionclassificationunderstandingthefundamentalsthroughhandsonlearning AT noesergio convolutionalneuralnetworkforskinlesionclassificationunderstandingthefundamentalsthroughhandsonlearning AT oterovinasmarta convolutionalneuralnetworkforskinlesionclassificationunderstandingthefundamentalsthroughhandsonlearning AT meicivan convolutionalneuralnetworkforskinlesionclassificationunderstandingthefundamentalsthroughhandsonlearning AT manzocarlo convolutionalneuralnetworkforskinlesionclassificationunderstandingthefundamentalsthroughhandsonlearning |