DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images by Jyoshna Allenki, Hemant Kumar Soni

“…The dilated convolution layers can retrieve fine spatial features, whereas Swin models are designed to obtain global information. Both these features are integrated by the MSMFD component that enhances segmentation. …”
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C²SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction by Di Wu, Yi Shi, Ziyu Wang, Jie Yang, Mohamad Sawan

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Catch Composition of Set Net Fisheries during Normal Season in Sungai Sembilang Waters, Selangor, Malaysia by Wan Muhammad Luqman bin Wan Rosdi, Abdul Wahab Abdullah, Mohd Nazir Taib, Noor Hanis Abu Halim, Nur Habibah Abu Talib, Hashim Shuhaimi, Sharum Yusof

“…This type of fishing gear can be used by local fisherman to catch fishes and can prevent trawl net from come closer to the shore. This study would provide some general useful information about the status of set net fisheries as well as the species abundance caught from the set net.…”
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…This paper presents DS-AdaptNet, an efficient segmentation framework that addresses these challenges through two synergistic technical innovations. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…To address these challenges, this paper proposes a multi-level hybrid lightweight water segmentation network, MLHI-Net. First, we design a convolutional module (ORRD) compatible with over-parameterized and redundancy removal techniques based on lightweight design. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

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UDCNet: A U-Net Guided Dual-Branch Cross-Attention Network for SAR Object Detection by Siyang Huang, Liushun Hu, Zhangjunjie Cheng, Shaojing Su, Junyu Wei, Xiaozhong Tong, Zongqing Zhao

“…Our method comprises three key components: 1) a U-Net guided dual-branch architecture with cross-attention module, which introduces a U-Net-based segmentation branch to generate “region-based hints” for cross-attention fusion with the detection branch; 2) Shi-Tomasi corner detection with convex polygon optimization for pseudo mask generation, enabling cost-effective segmentation masks from bounding label information, alleviating the need for exhaustive pixel-level annotations; and 3) a two-stage training strategy, which first jointly trains both branches, then freezes segmentation branch to finetune the detection backbone, mitigating gradient conflicts. …”
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Multi-spatial-attention U-Net: a novel framework for automated gallbladder segmentation on CT images by Henan Lou, Xiaobo Wen, Fanxia Lin, Zhan Peng, Qiuxiao Wang, Ruimei Ren, Junlin Xu, Jinfei Fan, Hao Song, Xiaomeng Ji, Huiyu Wang, Xiangyin Sun, Yinying Dong

“…Abstract Objective This study aimed to construct a novel model, Multi-Spatial Attention U-Net (MSAU-Net) by incorporating our proposed Multi-Spatial Attention (MSA) block into the U-Net for the automated segmentation of the gallbladder on CT images. …”
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Hyperspectral imaging combined with residual-attention-net for spectral-spatial feature fusion in liver disease diagnosis by Yunze Li, Jingjing Wang, Miaoqing Zhao, Jinlin Deng, Chongxuan Tian, Qize Lv, Yifei Liu, Kun Ru, Wei Li

“…This study aims to propose a novel hyperspectral image (400–1000 nm) processing method based on 3D-Residual-attention networks (3D Ra-Net) to improve the accuracy of differentiation between the two.The study employs a 3D Ra-Net model that integrates spectral features with spatial information to enhance classification accuracy. …”
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NetMHCphosPan - Pan-specific prediction of MHC class I antigen presentation of phosphorylated ligands by Carina Thusgaard Refsgaard, Carolina Barra, Xu Peng, Nicola Ternette, Morten Nielsen

“…The developed method is available at http://www.cbs.dtu.dk/services/NetMHCphosPan-1.0.…”
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The songbird connectome (OSCINE-NET.ORG): structure–function organization beyond the canonical vocal control network by Andrew Savoy, Katherine L. Anderson, Joseph V. Gogola

“…Results We present the Oscine Structural Connectome for Investigating NEural NETwork ORGanization (OSCINE-NET.ORG), the first interactive mesoscale connectome for any vocal learner. …”
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ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement by Zehui Wang, Chuangquan Chen, Junhua Li, Feng Wan, Yu Sun, Hongtao Wang

“…In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. …”
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A wavelet CNN with appropriate feed-allocation and PSO optimized activations for diabetic retinopathy grading by Chandrasekaran Raja, Santhosh Krishna B V, Balaji Loganathan, Sanjay Kumar Suman, L. Bhagyalakshmi, Mubarak Alrashoud, Jayant Giri, T. Sathish

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Next-Generation Text Summarization: A T5-LSTM FusionNet Hybrid Approach for Psychological Data by Bilal Khan, Muhammad Usman, Inayat Khan, Jawad Khan, Dildar Hussain, Yeong Hyeon Gu

“…In this context, this study proposes a new hybrid model T5-LSTM FusionNet, to enhance textual content summarization in the field of psychology. …”
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ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation by Chukwuemeka Clinton Atabansi, Hui Li, Sheng Wang, Jing Nie, Haijun Liu, Bo Xu, Xichuan Zhou, Dewei Li

“…The statistical and visual results demonstrate that the proposed ICT-Net outperforms other existing approaches investigated in this study in terms of ACC, DSC, and IoU. …”
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Transfer of Periodic Phenomena in Multiphase Capillary Flows to a Quasi-Stationary Observation Using U-Net by Bastian Oldach, Philipp Wintermeyer, Norbert Kockmann

“…The U-Net consists of four max pooling layers and four upsampling layers. …”
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SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative Biomarker by Suraj Kumar, Suman Hazarika, Cota Navin Gupta

“…SAGEFusionNet learns important ROIs for brain age prediction by incorporating ROI-aware pooling at every layer to overcome the above challenges. …”
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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices by Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, Likhit Sagar Gajja, Sravanthy Myneni, Akhil Dusi, Lalithya Posham

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Objective monitoring of Insecticide-treated bednet use to improve malaria prevention: SmartNet development and validation. by Paul J Krezanoski, Jeffrey I Campbell, Data Santorino, David R Bangsberg

“…We tested SmartNet among five volunteers using the device over their beds in Boston, USA for two weeks with the goal of evaluating device reliability, accuracy and acceptability to inform future device improvements. …”
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DynseNet: A Dynamic Dense-Connection Neural Network for Land–Sea Classification of Radar Targets by Jingang Wang, Tong Xiao, Kang Chen, Peng Liu

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