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检索条件"任意字段=Conference on Visualization in Biomedical Computing"

共 893 条记录，以下是131-140 订阅

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biomedical Image Classification Using Convolutional Neural Networks

Biomedical Image Classification Using Convolutional Neural N...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International conference

作者： Divya Sharma Ishani Mishra Ramanamma Parepalli Jayanth. S Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India

Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life.

关键词： Deep learning Analytical models Neural networks Data visualization Data models Internet Convolutional neural networks

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AI-Powered Smart Glasses for Sensing and Recognition of Human-Robot Walking Environments

AI-Powered Smart Glasses for Sensing and Recognition of Huma...

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IEEE/RAS-EMBS International conference on biomedical Robotics and Biomechatronics (BioRob)

作者： Daniel Rossos Alex Mihailidis Brokoslaw Laschowski Division of Engineering Science University of Toronto Toronto Canada KITE Research Institute Toronto Rehabilitation Institute Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mechanical and Industrial Engi-neering University of Toronto Toronto Canada

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Environment sensing and recognition can allow hu-mans and/or robotic systems to dynamically adapt to different walking terrains. However., fast yet accurate visual perception is challenging., especially on embedded systems with limited computational resources. The purpose of this study was to develop and prototype a new pair of integrated AI-powered smart glasses for onboard sensing and recognition of human-robot walking en-vironments with high accuracy and low latency. Our system in-cludes a Raspberry Pi Pico micro controller and an ArduCam low-power camera., both of which interface with commercial eye-glass frames via 3D-printed mounts that we custom-designed. We trained and optimized a lightweight and efficient convolutional neural network using a MobileN etVI backbone to classify real-world walking terrains as either indoor surfaces., outdoor surfaces (grass and dirt)., or outdoor surfaces (paved) using over 62,500 egocentric images that we adapted and manually labelled from the Meta Eg04D dataset. We compiled and deployed our deep learning model using TensorFlow Lite micro and post-training quantization to create a minimized byte array model of size 0.31MB. Our system was able to accurately classify complex walking environments with 93.6% accuracy and an embedded inference speed of 1.5 seconds during online experiments. These AI-powered smart glasses open new opportunities for visual per-ception of human-robot walking environments where real-time embedded computing is desired. Future research will focus on improving the onboard inference speed and further miniaturization of the mechatronic components.

关键词： Legged locomotion Adaptation models visualization Accuracy Quantization (signal) Robot sensing systems Real-time systems Sensors Smart glasses Visual perception

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Portable blood glucose meter system for self-monitoring of diabetic patients

Portable blood glucose meter system for self-monitoring of d...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International conference on

作者： Rosa Perez-Siguas Eduardo Matta-Solis Anika Remuzgo-Artezano Hernan Matta-Solis Hernan Matta-Perez Luis Perez-Siguas TIC Research Center: eHealth & eEducation Instituto Peruano de Salud Familiar Lima Peru

The appearance of COVID-19 changed the lifestyle of many people as it spread rapidly around the world, causing concern to the entire health system due to the high number of infected and leading to a general confinement, changing the lifestyle and eating habits of many people causing diabetes, which is a disease caused by the high level of glucose in the blood, which can generate serious problems in the health of the person since it has no cure, this progressive disease is controlled or monitored by conventional glucometer equipment that generates pain in patients because they require blood samples to measure glucose, worse for those diabetics who must have the measurement several times a day. In view of this problem, this article will make a portable blood glucose meter system for the self-monitoring of diabetic patients and determine the blood sugar level to visualize it by means of a screen, with this system the measurement will be made without pain and will show the value of the glucose level accurately, Helping diabetic patients who perform monitoring several times a day. Through the development of l system, it was observed that it works in the best way with an efficiency of 96.97% in the measurement of glucose, when comparing with others equipment glucometers obtained a relative error of 2.99%, being an error accepted to approach the real value.

关键词： Meters COVID-19 visualization Pain Glucose Diabetes biomedical monitoring

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The Deep Analysis of Accuracy of Performance Level of NBC Algorithm for Diagnosing BC

The Deep Analysis of Accuracy of Performance Level of NBC Al...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： P. Valarmathi M Ramchander Sampathirao Suneetha P. Sasirekha Saad Khudhur Mohammed Ahmed Hussain Department of CSE Mookambigai College of Engineering Department of MCA Chaitanya Bharathi Institute of Technology Hyderabad Department of Computer Science & Systems Engineering Andhra University College of Engineering Andhra University Andhra Pradesh Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

These days, breast cancer is the foremost predominant cancer among ladies around the world and one of the driving causes of cancer-related mortality. In this ponder, an existing Observation, The study of disease transmission, and Conclusion Comes about (Diviner) dataset is utilized to assess the survival rate of breast cancer patients. Numerous of the profound and machine learning strategies accessible nowadays are so great that they can illuminate a wide course of categorization issues based on precise consider of large-scale datasets. These advances are broadly utilized in most biomedical investigate disciplines. Careful information planning, counting both investigation and visualization, offers pivotal bits of knowledge to support basic decision-making within the field. In this article, Soothsayer breast cancer information is classified utilizing machine learning based strategies. Furthermore, a two-step highlight choice strategy utilizing Foremost Component Examination and Change Edge is utilized to extricate pertinent highlights from the dataset. The dataset is classified utilizing Administered and Outfit learning approaches, counting Ada Boosting, XG Boosting, Incline Boosting, Naive Bayes varieties, and Choice Tree calculations, once the chosen highlights have been removed from the breast cancer dataset. The diverse machine learning techniques are totally overviewed with the utilize of train-test portion and k fold cross-validation. In specific, the Choice Tree calculation works exceptionally well, showing an outstandingly tall accuracy rate in both the train-test part and cross-validation procedures. Subsequently, for the Diviner Breast Cancer dataset, this inquire about illustrates the predominance of the choice Tree over the other elective approaches in Administered and Outfit learning procedures and gives a valuable knowledge into moving forward clinical choice and expectation.

关键词： visualization Accuracy Decision making Boosting Breast cancer Bayes methods Planning

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AI and Cloud for Real-Time Evaluation of Sports Injury Severity and Recovery

AI and Cloud for Real-Time Evaluation of Sports Injury Sever...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Blessy, Y.M. Parvathy, Palaniraj Rajidurai Kishore Verma, S. Sreedevi, B. Jayaprakash, S. Murugan, S. R.M.K. Engineering College Department of Electornics and Communication Engineering Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Department of Computer Science and Engineering Telangana Hyderabad India Sri Sairam Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

This research provides a revolutionary artificial intelligence (AI) and cloud-based system for assessing the severity of sports injuries in real time and tracking healing progress. The objective is to develop an AI and Cloud-based system for evaluating sports injuries in real time, providing accurate severity assessment, recovery tracking, and personalised treatment recommendations with powerful Natural Language Processing (NLP) and image analysis algorithms. The proposed method uses modern NLP and deep learning-based image analysis algorithms to process and analyze data from a variety of sources, such as IoT-enabled wearable devices, medical reports, and athlete feedback. NLP is used to extract useful insights from unstructured text, while image analysis finds damage patterns in medical images. By combining these technologies with cloud computing, the system provides a scalable and economical solution for real-time injury assessment, including personalized rehabilitation programs and predictive modelling. AI enables automated, data-driven decision-making, which reduces human error while increasing diagnostic speed and accuracy. Future study will look at the addition of more AI algorithms, expanding the dataset to include more injury kinds, and improving the system's real-time feedback capability. This method aims to transform sports injury care and recovery optimization. © 2025 IEEE.

关键词： Personalized medicine

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MVCNet: A Vision Transformer-Based Network for Multi-Label Zero-Shot Learning in Medical Imaging

MVCNet: A Vision Transformer-Based Network for Multi-Label Z...

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Software Engineering and Artificial Intelligence (SEAI), IEEE International conference on

作者： Shengen Wu Hong-Seng Gan Ying-Tuan Lo Muhammad Hanif Ramlee Hafiz Basaruddin School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Suzhou China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China XJTLU Entrepreneur College (Taicang) Xi’an Jiaotong-Liverpool University Suzhou China Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia

ISBN: (数字)9798350374346

ISBN: (纸本)9798350374353

In medical imaging datasets, the high cost of annotation and incomplete coverage of disease categories are significant challenges. Multi-Label Generalized Zero Shot Learning (ML-G ZSL) holds promise for alleviating these issues in medical practice. ML-GZSL techniques can predict a variety of seen and unseen disease classes by learning the visual and semantic information of seen classes. Recently, Vision Transformer (ViT) has demonstrated considerable potential due to its structural similarity to Transformers. When ViT is used in conjunction with Transformers, it can generate better visual features and label embeddings. Inspired by these facts, we propose a ViT-based medical image ML-GZSL network, MVCNet. Specifically, we utilize ViT to capture both global and local features of images and employ BioBert, a Transformer-based model pretrained on biomedical corpora, to obtain label embeddings. Both global and local features are then compared with label embeddings in a novel voting fusion layer to compute similarities and produce the final classification predictions. Our method surpasses advanced baseline models across multiple performance metrics by using the public medical imaging dataset MedMNIST.

关键词： Training visualization Zero-shot learning Computational modeling Biological system modeling Transformers Usability

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AI-Powered Real-Time Big Data Analytics for ICU Resource Management

AI-Powered Real-Time Big Data Analytics for ICU Resource Man...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： S. Kalaichelvi N. Mohankumar C. Uthayakumar Barkathulla A K.S. Manojee S. Murugan Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Tiruchengode Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Vivekanandha College of Engineering For Women Tiruchengode Namakkal Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Improving patient outcomes and operational efficiency in intensive care units (ICUs) relies heavily on efficient resource management. This work presents a novel LSTM-based methodology for real-time optimization of ICU resources, using big data to enhance decision-making. Patient vitals, equipment utilization, and staff activities are just a few examples of the real-time data gathered from the many ICU sensors and monitoring devices the system incorporates using LSTM networks. LSTM networks excel in processing time-series data, allowing the system to anticipate resource requirements, spot bottlenecks, and allocate resources optimally. The proposed system offers predicted insights together with practical suggestions by analyzing historical and real-time data streams. Simulations and case studies show that the system can increase operational efficiency and predict demand. Findings point to major gains in patient care, savings, and decision-making assistance in fast-paced ICUs. This method uses LSTM networks to tackle the intricacies of contemporary hospital environments, revolutionizing ICU management with innovative AI and big data analytics. LSTM networks improve real-time decision-making, resource management, and patient outcomes in ICUs.

关键词： Analytical models Accuracy Decision making Predictive models Big Data Real-time systems Data models Resource management Long short term memory Load modeling

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Web application for real-time enhancement of low-light images using Zero-DCE and hybrid optimization

Web application for real-time enhancement of low-light image...

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International Symposium on Advanced computing and Communication (ISACC)

作者： U Kumaran Ashwin ER Naveenkumar V Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This paper presents the development of a web application designed for the real-time enhancement of low-light images using advanced deep learning techniques, specifically Zero-DCE (Zero-Reference Deep Curve Estimation) and hybrid optimization methods. Low-light images often suffer from noise, poor visibility, and detail loss, making it challenging to interpret them effectively. Traditional image enhancement methods do not perform well in real-time applications. To address this, the proposed web application leverages the Zero-DCE model, which requires no reference image, making it suitable for diverse real-world conditions. Further, a hybrid optimization approach, combining techniques such as adaptive learning rate adjustments, GPU acceleration, and a Generative Adversarial Network (GAN) for fine-tuning, is employed to enhance computational efficiency, output quality, and perceptual realism. The application processes images in real-time, restoring detail, reducing noise, and improving contrast, thereby significantly enhancing the visibility of objects in low-light conditions. The system’s user-friendly interface allows for easy uploading and real-time processing of images, providing an accessible solution for various fields, including security, medical imaging, and satellite imagery, where clear visual interpretation is critical. We also evaluate the model’s performance on a diverse set of low-light images and compare it against state-of-the-art methods. An analysis of the computational requirements and user experience is also provided.

关键词： Deep learning visualization Computational modeling Generative adversarial networks Real-time systems User experience Image restoration Satellite images Computational efficiency biomedical imaging

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HLSM-UNet:Hybrid Local Spatial Mamba UNet for Medical Image Segmentation

HLSM-UNet:Hybrid Local Spatial Mamba UNet for Medical Image ...

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Neuromorphic computing (ICNC), International conference on

作者： Xude Zhang Xiaoping Wu Changzhen Zhang Yumin Tang Dihong Luo Houbing Tang Micro-nano and Intelligent Manufacturing Engineering Research Centre of Ministry of Education Kaili University Guizhou China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In the field of Medical Image Segmentation, deep learning models, especially Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models, have been widely researched and applied in recent years. However, these two mainstream methods each face some challenges. CNN occupies a place in medical image processing with its powerful local feature extraction ability, but it is inadequate in dealing with global or remote feature dependencies, which to some extent limits its performance in complex medical image segmentation tasks. The ViT model, through its Self-Attention mechanism, can effectively capture long-range dependencies in images, but it also comes with high computational costs, especially the secondary computational complexity, which becomes a bottleneck that cannot be ignored when processing high-resolution medical images. In this paper, we propose a medical image segmentation method called HLSM-UNet based on a State Space Model(SSM). This model combines the advantages of UNet with the remote modeling capability of the Mamba model, aiming to achieve more efficient and accurate medical image segmentation. We conducted experiments on the publicly available ACDC MRI heart segmentation dataset and Prostate dataset, and the results showed that HLSM UNet outperforms existing CNN and Transformer models in multiple evaluation metrics. Especially in terms of remote modeling capability and computational efficiency, significant advantages have been demonstrated. This not only proves the effectiveness of the state space model in medical image segmentation, but also provides new ideas and methods for subsequent research.

关键词： Image segmentation visualization Neuromorphic engineering Computational modeling Transformers Feature extraction Computational efficiency Convolutional neural networks Time complexity biomedical imaging

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress biomedical imaging

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