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Liver Tumor Segmentation and Classification Using Deep Learning 5

Liver Tumor Segmentation and Classification Using Deep Learn...

引用

5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Amritha, M. Manimegalai, P. Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9781665493604

the high variation in the size of tumor, location, depth, intimate boundaries of other organs around liver, liver tumor segmentation is the most important yet challenging task. the size and other characteristics of liver computed tomography (CT) scan vary, including the number of layers in each one. thus, obtaining liver CT scans with varying parameters is a difficult task. As a result, the project includes an effective method for segmenting liver tumors using 3D Unet, a Convolutional Neural Network. By generating suggestions about the tumor region, the proposed method, which is based on the RESNET approach, efficiently identifies tumors in the liver. In the preprocessing step, the proposed method creates a cardinal outline of the liver by converting CT volumes to normalized CT image slices. For feature extraction, normalized CT images are then fed to Resnet. A 3D FRN method is used to classify the data. the dataset, which includes 13000 CT scans of various patients taken at various clinical sites, is freely accessible on the LiTS challenge website. the CT image dataset is used to train the proposed method. this quickly classifies and diagnoses liver tumors by combining image processing techniques with Convolutional Neural Network (CNN) algorithms. © 2023 IEEE.

关键词： Convolutional neural networks

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Remaining Useful Life Prediction of Rolling Bearings Based on Global-Local Attention Mechanism Transformer with Multi-Source Feature-Weighted Kernel Principal Component 5

Remaining Useful Life Prediction of Rolling Bearings Based o...

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5th international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Xie, Zaimi Mo, Chunmei Jia, Baozhu Naval Architecture and Shipping College Guangdong Ocean University Zhanjiang China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Zhanjiang China Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching Zhanjiang China College of Electronic and Information Engineering Guangdong Ocean University Zhanjiang China Guangdong Engineering Technology Research Center of Intelligent Ocean Sensor Network and Equipment Zhanjiang China

ISBN: (纸本)9798331529192

Aiming at the health index (HI) is difficult to characterize when the rolling bearing state changes and the transformer network attention mechanism only focuses on the global feature information, resulting in the low prediction performance. this paper proposes a bearing RUL prediction method based on the global-local attention mechanism transformer with a multi-source feature-weighted kernel principal component. Squeeze and excitation networks (SENet) are applied to the adaptive assign weights to its multi-channel features. It combined with the distances of the multi-source features, which were calculated by the Mahalanobis distance (MD), to obtain the multi-source HI through the kernel principal component analysis algorithm (KPCA). It can balance multi-source features and retain important feature information. the global-local attention mechanism transformer method fuse and learn global and local spatiotemporal sequence features can improve bearing RUL prediction model performance. the proposed method was verified by experiments of the XJTU-SY dataset and the PHM2012 dataset and compared with the publicized methods. Results demonstrate the effectiveness and superiority of the proposed approach in terms of prediction accuracy. © 2024 IEEE.

关键词： Roller bearings

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Fog Computing for Deep Learning with Pipelines 7

Fog Computing for Deep Learning with Pipelines

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7th IEEE international conference on Fog and Edge Computing (ICFEC) / 23rd IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGRID)

作者： Vainio, Antero Mudvari, Akrit Kiedanski, Diego Tarkoma, Sasu Tassiulas, Leandros Univ Helsinki Helsinki Finland Yale Univ New Haven CT USA

ISBN: (纸本)9798350322880

In this article, we introduce a fog system design for processing data collected from edge devices, such as mobile, sensor, and extended (mixed, augmented, virtual) reality equipment. Our system enables the network to provide hardware-accelerated processors for resource-intensive computations on data gathered from remote locations, such as 5G and beyond mobile networks. By splitting heavy computations into pipelines, and distributing them among processors in the edge, fog and the cloud, our design benefits from the processing power of the cloud, while utilizing fog devices with a lower network latency. We implement our design, and use it for distributed training and inference with industry-grade deep learning models for computer vision. We deploy our architecture in infrastructure including cloud and edge servers supporting GPU-accelerated computations. We benchmark pipelines in various deployment settings to study the overhead that they introduce. Our contributions are a new design for wide-area data processing, a framework that realizes this design and provides means of developing applications that are optimized in terms of infrastructure and hardware. these contributions are complemented with our benchmark results, which reveal the potential causes of processing overhead.

关键词： fog computing stream processing systems deep learning

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5th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2022

5th International Conference on Recent Trends in Image Proce...

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5th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2022

ISBN: (纸本)9783031235986

the proceedings contain 31 papers. the special focus in this conference is on Recent Trends in Image processing and Pattern Recognition. the topics include: Image processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood;An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity;motor Imagery Classification Combining Riemannian Geometry and Artificial Neural networks;Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201;shrimp Shape Analysis by a Chord Length Function Based Methodology;supervised Neural networks for Fruit Identification;targeted Clean-Label Poisoning Attacks on Federated Learning;building Marathi SentiWordNet;A Computational Study on Calibrated VGG19 for Multimodal Learning and Representation in Surveillance;alzheimer’s Disease Detection Using Ensemble Learning and Artificial Neural networks;automated Deep Learning Based Approach for Albinism Detection;a Deep Learning-Based Regression Scheme for Angle Estimation in Image Dataset;the Classification of Native and Invasive Species in North America: A Transfer Learning and Random Forest Pipeline;Towards a Digital Twin Integrated DLT and IoT-Based Automated Healthcare Ecosystem;enabling Edge Devices Using Federated Learning and Big Data for Proactive Decisions;ioT and Blockchain Oriented Gender Determination of Bangladeshi Populations;Federated Learning Based Secured Computational Offloading in Cyber-Physical IoST Systems;a Hybrid Campus Security System Combined of Face, Number-Plate, and Voice Recognition;single-Trial Detection of Event-Related Potentials with Artificial Examples Based on Coloring Transformation;identifying the Relationship Between Hypothesis and Premise;semi-supervised Multi-domain Learning for Medical Image Classification;data Poisoning Attack by Label Flipping on SplitFed Learning;a Deep Learning-Powered

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Distributed sensor-Assisted DNN-Based Massive-MIMO Channel Reconstruction 5

Distributed Sensor-Assisted DNN-Based Massive-MIMO Channel R...

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5th international Science and Technology conference Modern Network Technologies, MoNeTec 2024

作者： Artemasov, Dmitry Sherstobitov, Alexander Lyashev, Vladimir Skolkovo Institute of Science and Technology Center for Next Generation Wireless and IoT Moscow Russia Moscow Institute of Physics and Technology The Department of Multimedia Technology and Telecommunication Moscow Russia

ISBN: (纸本)9798350369007

Modern massive multiple-input multiple-output (MIMO) systems allow for a great increase in cell throughput by means of spatial multiplexing. However, urban environmental conditions, such as low signal-to-noise ratio (SNR) and non-line-of-sight (NLoS) cell areas, can limit their full potential. To overcome these issues, techniques such as beam sweeping and beam tracking may be used to establish the correct beamforming for signal reception. Nevertheless, beam sweeping can induce overhead in terms of the time required to scan the site area, and beam tracking can only be performed after the initial connection, what may restrict the practical application of these methods. In this paper, we propose to utilize the set of passive sensors equipped with small antenna arrays to receive the uplink pilot signals and apply the state-of-the-art deep neural network transformer architecture for the base station (BS) channel state information (CSI) reconstruction task. We show that it is possible to recover the BS high-dimensional (128 ports) CSI in the negative SNR region with the help of passive synchronized sensors equipped with 4 cross-polarized antenna arrays. the developed method allows for the reconstruction of the 1 transmission layer precoding matrix with an accuracy of approximately 84% for more than 70% of UEs deployed in the NLoS area, while being trained on only 10% of samples. © 2024 IEEE.

关键词： Deep neural networks

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Research on style transfer model based on improved CycleGAN algorithm 5

Research on style transfer model based on improved CycleGAN ...

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5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Bi, Xiaoyang He, Enhao Wang, Qipeng Xiamen University Malaysia School of Electrical Engineering and Artificial Intelligence Selangor Malaysia

ISBN: (纸本)9798350310603

Style transfer is a heated computer vision topic that has been broadly mentioned in both academic research and practical applications, for example, the style transfer technique is behind the various filters in daily used image processing tools. However, it had been a major drawback in this field that its training set required strictly paired images, until the introduction of CycleGAN, a combination of style transfer technique and Generating Adversarial networks (GAN), which has also been one of the most popular technologies in machine learning since its advent. CycleGAN is an image style transfer technique proposed by Jun-Yan Zhu et al. at the University of California, Berkeley. the paper proposed a way that can be performed without the strict pairs of the source image and style image so that a wider range of dataset options can be incorporated into this field. the generator in the CycleGAN network consists of an encoder, converter, and decoder, which can play the role of preserving the original image features and converting images. In our research, this study found that there is still certain scope for improvement in the original model, so this study conducted a further study on the basis of rebuilding the original CycleGAN. this study studied the CycleGAN framework, which is an unsupervised learning style transfer model based on generative adversarial networks, and proposed an improved nerual network model by modifying the adversarial loss, and compared it with the original CycleGAN network. the results showed that the modified CycleGAN can obtain more realistic images with better visual effects than original CycleGAN after training in fewer rounds. © 2023 IEEE.

关键词： Generative adversarial networks

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

5th International Conference on Medical Imaging and Computer...

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

ISBN: (纸本)9789819638628

the proceedings contain 53 papers. the special focus in this conference is on Medical Imaging and Computer Aided Diagnosis. the topics include: Deep Learning Frameworks for Histopathological Image processing in Colorectal Cancer Diagnostics;Improving Knee Osteoarthritis Detection through a Multitask Learning Method from 2D MRI Slices;enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study;retNet: A Two-Stage Deep Neural Network for Clinically Traceable Retinal Disease Classification;Attention-Based CNN for Enhanced Detection of Arsenic Exposure;deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma;Knowledge Distillation for Enabling Efficient AI-Based Skin Cancer Detection in Resource-Constrained Environments;classification Method in Vision Transformer with Explainability in Medical Images for Lung Neoplasm Detection;BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural networks for Medical Image Classification;increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches;Synth-to-Segment: MRI Brain Tumor Segmentation with Diffusion Transformers and Attention U-Net;(ExMod) Model for Medical Image Segmentation Using Scribble Annotations;LV-Mamba: Integrating Denoising Mechanism with Mamba for Improved Segmentation of the Pediatric Echocardiographic Left Ventricle;LoRA-MedSAM: Efficient Medical Image Segmentation;dependency-Related Skin Lesion Bed and Periwound Segmentation Trained on Partially Annotated Clinical Images;rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion;Explainable Deep Learning Framework for Ground Glass Opacity (GGO) Segmentation from Chest CT Scans;Multi-statistical Features-Based 4D Spatiotemporal Level Set Model for CTP Image Segmentation;deep Learning Based Segmentation of Magnetic Resonance Cardiac Images;generic Liv

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Multi-Scale Convolutional Vision Transformer for Semantic Segmentation 5

Multi-Scale Convolutional Vision Transformer for Semantic Se...

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5th international conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Otyrba, Rostislav R. Sirota, Alexander A. Voronezh State University Faculty of Computer Sciences Department of Information Security and Processing Technologies Voronezh Russia

ISBN: (纸本)9798350309126

Semantic segmentation is a fundamental task in computer vision, and the development of encoder networks remains one of the primary directions in this field. Encoder networks play a crucial role since the final result of segmentation directly depends on the quality of the extracted image features. this paper presents a novel hierarchically structured encoder architecture that integrates Convolutional Neural networks with multi-scale feature capturing and Transformers. the proposed model introduces three significant architectural solutions. First, it introduces a new Self-Attention Block that includes an effective Inception-like multi-branch convolution layer with a Multi-Head Self-Attention layer. Second, the computational complexity of Multi-Head Self-Attention is efficiently reduced by resizing the spatial dimensions of Key and Value tokens using Average Pooling. third, it abandons positional encoding and adopts a Depth-Wise convolutional layer in the Feed-Forward Network instead. We conducted experiments on datasets of varying complexity for image classification, such as CIFAR-100 and Caltech-256, to assess the generalization ability and feature extraction efficiency of the network. It demonstrates competitive performance, achieving an accuracy of 0.775 on CIFAR-100 and 0.665 on Caltech-256, compared to state-of-the-art Transformers, while having lower model complexity. these results demonstrate the great potential of our model and underline the need for further research aimed at its refinement, the search for the best architecture characteristics, pre-training on the ImageNet-1K dataset, and its eventual integration into semantic segmentation tasks. © 2023 IEEE.

关键词： Semantic Segmentation

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Ship Noise Target Classification Based on Deep Convolutional networks 5

Ship Noise Target Classification Based on Deep Convolutional...

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5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Zhang, Lei Sun, Lu Yu, Jincheng Shu, Jiangzheng Zhejiang Zhoushan Marine Power Research Institute Co. Ltd. Zhoushan Power Supply Company State Grid Zhejiang Electric Power Company Zhoushan China Ocean College Zhejiang University Zhoushan China

ISBN: (纸本)9798331530334

Due to the presence of not only a large amount of natural and anthropogenic noise in the ocean, but also the undulating and variable seabed and inhomogeneous seawater layer, which makes it more difficult to recognize and classify targets in the ocean acoustic field. In recent years, most of the research results in related fields are based on private datasets, but the private datasets are not publicly available and the data volume is small, which hinders the development of the field of underwater acoustic target recognition. the use of deep learning for hydroacoustic signal processing is a current trend, so this paper proposes a method for underwater noise classification using a deep convolutional network model (AlexNet), which will extract three features, namely, the short-time spectrum, the FBank spectrum, and the Mel cepstrum coefficients, of underwater ship noise. the data is obtained using a large open-source hydroacoustic dataset, DeepShip, which consists of 47 hours and 4 minutes of real-world underwater recordings from 265 different vessels in four categories. the classification results of different acoustic features are firstly compared and analyzed based on deep convolutional network type, and the classification results under different features are evaluated by parameters such as accuracy, precision, recall and F1 score. then the FBank spectra with the best classification results were analyzed, and the effects of different model hyperparameters on classification accuracy were investigated. © 2024 IEEE.

关键词： Underwater acoustics

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Valluvan: processing Name Board Images to Enhance Communication for Native Tamil Speakers 1

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

the Valluvan app is a language solution for native Tamil speakers. the system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. the app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. the Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. the paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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