Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 57 references
Computer Science
TL;DR
A new loss function is proposed that enhances conventional loss functions such as IoU loss, SSIM Loss, and Focal loss function with Active Contour Euler Elastical loss with effectiveness in enhancing segmentation accuracy and boundary delineation of ovarian ultrasound images.
The proposed improved U-Net-based deep learning model for image segmentation provides an effective solution for automated, high-precision segmentation of breast ultrasound images, demonstrating considerable potential for clinical translation.
Dense State Space Selection U-Net, a dense state space selection network, is proposed, to enhance liver tumor segmentation from CT images, by integrating a gating mechanism and dense state space blocks, resulting in superior segmentation accuracy.
The results demonstrate that the dual-encoder architecture, MSCAFM, and DAFM all contribute positively to the overall segmentation performance, and DCLA-Net consistently achieves superior performance across all evaluation metrics on both datasets.
Yan Cheng, Xiao-Kang Ding, Wei-Wei Zhu· Frontiers in Physiology· 0 citations
An attention-controlled automated liver tumor segmentation and classification based on a customized Mask Region Convolutional Neural Network with an addition of Multi-Scale Attention Gate (MSAG) is introduced with affirm the usefulness and clinical appropriateness of the suggested framework.
Babeetta Bbhagat, Mohini Kumbhar, Swati Powar et al.· Journal of Intelligent Decis...· 0 citations
A 3D U-Net-based segmentation framework, NeuroFocusNet, which synchronizes spatial attention, channel recalibration, and attention-gated skip connections within a single volumetric encoder-decoder model is proposed, which is encouraging when considering the feasibility of using NeuroFocusNet as a technically promising...
Faizan Ullah, Z. Abbas, Sergo Gegechkori et al.· IEEE Access· 0 citations
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Musa Doğan, I. Ozkan· BMC Medical Imaging· 0 citations
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