TY - GEN
T1 - Selective Attention UNet for Segmenting Liver Tumors
AU - Patil, Darshan
AU - Sakarkar, Gopal
AU - Khatri, Pearl
AU - Khedkar, Atharva
AU - Gan, Hong Seng
AU - Ramlee, Muhammad Hanif
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://github.com/darshan8850/Liver-tumor-Segmentation
AB - In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://github.com/darshan8850/Liver-tumor-Segmentation
KW - Computer Vision
KW - Deep Learning
KW - Image Processing
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85179006138&partnerID=8YFLogxK
U2 - 10.1109/ICCSI58851.2023.10303778
DO - 10.1109/ICCSI58851.2023.10303778
M3 - Conference Proceeding
AN - SCOPUS:85179006138
T3 - ICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence
SP - 44
EP - 49
BT - ICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023
Y2 - 20 October 2023 through 23 October 2023
ER -