Naive Bayes Learning of Dermoscopy Images

Surówka, G.; Ogorzalek, M.J.

doi:10.1007/978-3-030-20915-5_27

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2022

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“…Surówka et al (Surówka and Ogorzalek, 2019) have extracted wavelet features appropriate for the classification of dermoscopy images into two classes: pigment skin cancer (melanoma) and dysplastic (atypical but benign) skin lesion. In their work for feature extraction, the wrapper method will be used for classification with the Naive Bayes.…”

Section: Related Workmentioning

confidence: 99%

Multi-features extraction based on deep learning for skin lesion classification

2022

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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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Section: Related Workmentioning

confidence: 99%