Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
Significant advances have been made in augmented reality (AR) technology, which provides immersive experiences by superimposing digital content over the real world. However, there are issues with how Head-Mounted Disp...
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This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical...
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This paper put forward an embedded scheme to execute image watermarking in light of the discrete wavelet transform (DWT), singular value decomposition (SVD) and Charge System Search (CSS) method. In the proposed schem...
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A Flying Ad-hoc Network (FANET) is a group of Unmanned Aerial Vehicles (UAVs) that operate in an ad-hoc fashion, like Mobile Ad-hoc networks (MANET) and Vehicle Ad-hoc networks (VANET). The application of UAVs ha...
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The potential depletion of oil resources, combined with the limited biodegradability of mineral oil-based lubricants, has highlighted the importance of developing bio-based lubricants. As a result, vegetable oils have...
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BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture imag...
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An educational studies and understanding about theoretical and practical laboratory in power quality issues. Interline dynamic voltage restorer is used in the proposed system for mitigating voltage sag, swell, harmoni...
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In this work, a comprehensive experiment is conducted to investigate the spatio-temporal variability of young wind waves under steady wind forcing. The experimental setup included a wave tank equipped with a wind blow...
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