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- W3189174900 abstract "Deep learning algorithms using convolutional neural networks can diagnose melanoma with similar accuracy to expert dermatologists. These can be repurposed to guide decision-making about the need for lesion excision. A deep neural network was developed using the ResNet-152 architecture and a training dataset of 10 000 dermoscopic images of benign and malignant pigmented lesions. This approach showed excellent performance at melanoma diagnosis with an area under the receiver operating characteristic curve (AUROC) of 0.95. This result is similar to state-of-the-art algorithms reported in the literature. However, performance of the network at the decision support task was superior, with an AUROC of 0.99. Comparison of sensitivity-specificity trade-off curves and diagnostic odds ratios suggests that decision support may be a more appropriate application of deep learning than melanoma diagnosis. This may also be a more practically useful implementation of machine learning, allowing inexperienced clinicians and general practitioners to simulate the biopsy decision-making of an expert." @default.
- W3189174900 created "2021-08-16" @default.
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- W3189174900 date "2021-01-01" @default.
- W3189174900 modified "2023-09-23" @default.
- W3189174900 title "Melanoma decision support using deep learning" @default.
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