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• W3202319039 abstract "Skin is the first line of defense of the human body. Because the skin is exposed to the outside and suffersvarious aggressions , Skin cancer is the most common cancer. Accurate skin lesions image segmentation is essential for skin disease diagnosis and treatment planning. In order toimprove the segmentation results of the recently proposed comprehensive attention convolutional neural network(CA-Net) for skin lesions image segmentation, In this work, we propose a modified medical image segmentation network—modified comprehensive attention convolutional neural network (mCA-Net) to further improve segmentation performance. In particular, we create a new multi-scale channel attention module—MS-CA, which can display more accurate and relevant feature channels on multiple scales. The experiments showsthat our work greatly improve the average segmentation Dice score, accuracy, mean ASSD and mIoU andenhance the stability of the segmentation model. Through comprehensiveexperiments on the ISIC 2018 skin lesiondatasets, it is found that our proposed mCA-Netnetwork compared with CA-Net,improve the average segmentation Dice score from 92.08% to 93.56%, the average accuracy score of skin lesions from 92.68% to 93.32% and the mIoU from 85.32% increased to 87.89%. The segmentation results have been significantly optimized." @default.
• W3202319039 created "2021-10-11" @default.
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• W3202319039 date "2021-09-28" @default.
• W3202319039 modified "2023-10-17" @default.
• W3202319039 title "mCA-Net: modified comprehensive attention convolutional neural network for skin lesion segmentation" @default.
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• W3202319039 doi "https://doi.org/10.1080/21681163.2021.1978867" @default.
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