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Article Dans Une Revue Tissue and Cell Année : 2022

Multi-Features Extraction Based on Deep Learning for Skin Lesion Classification

Résumé

For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71%) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.
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Dates et versions

hal-03457466 , version 1 (30-11-2021)

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Samia Benyahia, Boudjelal Meftah, Olivier Lézoray. Multi-Features Extraction Based on Deep Learning for Skin Lesion Classification. Tissue and Cell, 2022, 74, pp.101701. ⟨10.1016/j.tice.2021.101701⟩. ⟨hal-03457466⟩
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