Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images
Deep learning is a branch of artificial intelligence that uses computational networks inspired by the human brain to extract patterns from raw data. Development and application of deep learning methods for image analysis, including classification, segmentation, and restoration, have accelerated in t...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | UVic-UCC |
| Repositorio: | RiUVic. Repositori institucional de la UVic-UCC |
| OAI Identifier: | oai:dspace.uvic.cat:10854/7665 |
| Acceso en línea: | http://hdl.handle.net/10854/7665 https://doi.org/10.1016/j.jid.2019.12.029 |
| Access Level: | acceso abierto |
| Palabra clave: | Xarxes neuronals (Neurobiologia) |
| Sumario: | Deep learning is a branch of artificial intelligence that uses computational networks inspired by the human brain to extract patterns from raw data. Development and application of deep learning methods for image analysis, including classification, segmentation, and restoration, have accelerated in the last decade. These tools have been progressively incorporated into several research fields, opening new avenues in the analysis of biomedical imaging. Recently, the application of deep learning to dermatological images has shown great potential. Deep learning algorithms have shown performance comparable with humans in classifying skin lesion images into different skin cancer categories. The potential relevance of deep learning to the clinical realm created the need for researchers in disciplines other than computer science to understand its fundamentals. In this paper, we introduce the basics of a deep learning architecture for image classification, the convolutional neural network, in a manner accessible to nonexperts. We explain its fundamental operation, the convolution, and describe the metrics for the evaluation of its performance. These concepts are important to interpret and evaluate scientific publications involving these tools. We also present examples of recent applications for dermatology. We further discuss the capabilities and limitations of these artificial intelligence-based methods. |
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