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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document : 80 / 80

한글제목(Korean Title) Skin Lesion Segmentation with Codec Structure Based Upper and Lower Layer Feature Fusion Mechanism
영문제목(English Title) Skin Lesion Segmentation with Codec Structure Based Upper and Lower Layer Feature Fusion Mechanism
저자(Author) Muhammad Hasnain   Imran Ghani   Muhammad F. Pasha   Seung R. Jeong   Cheng Yang   GuanMing Lu  
원문수록처(Citation) VOL 15 NO. 12 PP. 0060 ~ 0079 (2021. 12)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attentionmodule (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.
키워드(Keyword) Users’ trust   machine learning   REPTree   fuzzy rules   trust prediction   semantic segmentation   skin lesion segmentation   deep learning   convolutional neural network (CNN)   atrous spatial pyramid pooling  
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