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- W3083189971 abstract "When treating diseases involving skin lesions, properly assessing the severity of the disease iscrucial in establishing a correct treatment plan. To do this, physicians classify cases based onguidelines. These guidelines often are a trade-off between consistent and quick assessment ofthe case. This leaves room for improvement on both sides.In order to speed up the assesment process and make it more consistent, the possibili-ties of neural networks are explored. There are two ways in which neural networks are usedto analyze images of skin lesion: segmentation and classification. Segmentation is used todetect and to localize the lesion area within the image. It is commonplace in medical imageresearch and has been done on skin lesions before. Classification is used to indicate the severityof several aspects of a lesion or the disease itself. This is much rarer and mostly not in line withexisting standards used by physicians, such as the ABCDE score for skin cancer and PASI scorefor psoriasis.The goal of this research is to explore whether neural networks can be used to classifyskin lesions in line with existing medical standards. Segmentation is also used to try andsupport the classification process. Due to the availability of data, the segmentation will focuson image of lesions regarding skin cancer, while classification focuses on psoriasis lesions andthe corresponding PASI score. While these two parts are not directly connected, they exploreadjacent applications. In the end, it is examined to see whether these two applications could beused together to improve classification results in the future." @default.
- W3083189971 created "2020-09-11" @default.
- W3083189971 creator A5039185486 @default.
- W3083189971 date "2020-01-01" @default.
- W3083189971 modified "2023-09-26" @default.
- W3083189971 title "Segmentation and classification of skin lesions using neural networks" @default.
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- W3083189971 hasPublicationYear "2020" @default.
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