Abstract
In the recent era, deep learning has become a crucial technique for the detection of various forms of skin lesions. Indeed, Convolutional neural networks (CNN) have became the state-of-the-art choice for feature extraction. In this paper, we investigate the efficiency of three state-of-the-art pre-trained convolutional neural networks (CNN) architectures as feature extractors along with four machine learning classifiers to perform the classification of skin lesions on the PH2 dataset. In this research, we find out that a DenseNet201 combined with Cubic SVM achieved the best results in accuracy: 99% and 95% for 2 and 3 classes, respectively. The results also show that the suggested method is competitive with other approaches on the PH2 dataset.
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Acknowledgement
This work was completed as part of the Hubert Curien Partnership (PHC) TASSILI cooperation program between France and Algeria under the project code 19MDU212.
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Benyahia, S., Meftah, B., Lézoray, O. (2021). Skin Lesion Classification Using Convolutional Neural Networks Based on Multi-Features Extraction. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_45
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DOI: https://doi.org/10.1007/978-3-030-89128-2_45
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