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- W3204232619 abstract "In dermatology, malignant melanoma is one the most deadly forms of skin cancer. It is extremely important to detect it at an early stage. One of the methods of detecting it is an evaluation based on dermoscopy combined with one of the criteria for assessing a skin lesion. Such an evaluation method is the Three-Point Checklist of Dermoscopy which is considered a sufficient screening method for the assessment of skin lesions. The proposed method, founded on the convolutional neural networks, is aimed at improving diagnostics and enabling the preliminary assessment of skin lesions by a family doctor. The current paper presents the results of the application of convolutional neural networks: VGG19, Xception, Inception-ResNet-v2, for the assessment of skin lesions asymmetry, along with various variations of the PH2 database. For the best CNN network, we achieved the following results: true positive rate for the asymmetry 92.31%, weighted accuracy 67.41%, F1 score 0.646 and Matthews correlation coefficient 0.533." @default.
- W3204232619 created "2021-10-11" @default.
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- W3204232619 date "2021-01-01" @default.
- W3204232619 modified "2023-09-27" @default.
- W3204232619 title "Classification of Dermatological Asymmetry of the Skin Lesions Using Pretrained Convolutional Neural Networks" @default.
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- W3204232619 doi "https://doi.org/10.1007/978-3-030-87897-9_1" @default.
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