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EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation by Iqbal, Shahzaib; Ahmed, Hasnat; Sharif, Muhammad; Hena, Madiha; Khan, Tariq M.; Razzak, Imran is a scholarly article available to read on EtoBox.

What is EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation about?

Segmenting ultrasound images is critical for various medical applications, but it offers significant challenges due to ultrasound images' inherent noise and unpredictability. To address these challenges, we proposed EUIS-Net, a CNN network designed to segment ultrasound images efficiently and precisely. The proposed EUIS-Net utilises four encoder-decoder blocks, resulting in a notable decrease in computational complexity while achieving excellent performance. The proposed EUIS-Net integrates both channel and spatial attention mechanisms into the bottleneck to improve feature representation and collect significant contextual information. In addition, EUIS-Net incorporates a region-aware attention module in skip connections, which enhances the ability to concentrate on the region of the injury. To enable thorough information exchange across various network blocks, skip connection aggregation is employed from the network's lowermost to the uppermost block. Comprehensive evaluations are conducted on two publicly available ultrasound image segmentation datasets. The proposed EUIS-Net achieved mean IoU and dice scores of 78. 12\%, 85. 42\% and 84. 73\%, 89. 01\% in the BUSI and DDTI data

Author

Iqbal, Shahzaib; Ahmed, Hasnat; Sharif, Muhammad; Hena, Madiha; Khan, Tariq M.; Razzak, Imran

Published

2024

Language

EN