Attention Res-UNet
نویسندگان
چکیده
During a dermoscopy examination, accurate and automatic skin lesion detection segmentation can assist medical experts in resecting problematic areas decrease the risk of deaths due to cancer. In order develop fully automated deep learning model for segmentation, authors design Attention Res-UNet by incorporating residual connections, squeeze excite units, atrous spatial pyramid pooling, attention gates basic UNet architecture. This uses focal tversky loss function achieve better trade off among recall precision when training on smaller size lesions while improving overall outcome proposed model. The results experiments have demonstrated that this design, evaluated publicly available ISIC 2018 dataset, outperforms existing standard methods with Dice score 89.14% IoU 81.16%; achieves recall. also performed statistical test other is statistically significant.
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ژورنال
عنوان ژورنال: International Journal of Decision Support System Technology
سال: 2022
ISSN: ['1941-630X', '1941-6296']
DOI: https://doi.org/10.4018/ijdsst.315756