Imagen de Portada

Deep Learning Approaches for Skin Lesion Detection

Guardado en:
Publicado en:Electronics vol. 14, no. 14 (2025), p. 2785-2809
Autor principal: Vieira, Jonathan
Otros Autores: Mendonça Fábio, Morgado-Dias, Fernando
Publicado:
MDPI AG
Materias:
Datasets
Classification
Artificial intelligence
Applications programs
Artificial neural networks
Cancer
Neural networks
Skin cancer
Mobile computing
Image transmission
Lesions
Deep learning
Machine learning
Client server systems
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools.
ISSN:2079-9292
DOI:10.3390/electronics14142785
Fuente:Advanced Technologies & Aerospace Database