NevusCheck: A Dysplastic Nevi Detection Model Using Convolutional Neural Networks †
Dysplastic nevi are skin lesions that have distinctive clinical features and are considered risk markers for the development of melanoma, the deadliest type of skin cancer. A specific deep learning technique to identify diseases is convolutional neural networks (CNNs) because of their great capacity...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | Perú |
| Institución: | Universidad Peruana de Ciencias Aplicadas |
| Repositorio: | UPC-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorioacademico.upc.edu.pe:10757/684677 |
| Acceso en línea: | https://doi.org/10.3390/engproc2025083011 http://hdl.handle.net/10757/684677 |
| Access Level: | acceso abierto |
| Palabra clave: | convolutional neural networks deep learning dysplastic nevus image classification melanoma skin cancer skin lesion https://purl.org/pe-repo/ocde/ford#3.00.00 |
| Sumario: | Dysplastic nevi are skin lesions that have distinctive clinical features and are considered risk markers for the development of melanoma, the deadliest type of skin cancer. A specific deep learning technique to identify diseases is convolutional neural networks (CNNs) because of their great capacity to extract features and classify objects. Therefore, the research aims to develop a model to diagnose dysplastic nevi using a deep learning network whose classification is based on the pre-trained architecture EfficientNet-B7, which was selected for its high classification accuracy and low computational complexity. As for the results obtained, an accuracy of 78.33% was achieved in the classification model. Also, the degree of similarity between the detection by a dermatology expert and the proposed model reached an accuracy of 79.69%. |
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