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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31-40 订阅

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

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Cybersecurity, Internet of Things and Soft computing (CITSC), Asia-Europe conference on

作者： Manping Qin Yong Xu Choon Kit Chan Zhiling Chen Caitong Zhu School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Faculty of Engineering and Quantity Surveying INTI International University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. Real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring.

关键词： Pregnancy Wireless communication Bluetooth Databases Data visualization Fetus Real-time systems biomedical monitoring Monitoring Smart phones

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GUI Toolkit for Pulmonologists: Age-Gender Specific Lung Sound Analysis and Disease Identification Using Sequence Modeling

GUI Toolkit for Pulmonologists: Age-Gender Specific Lung Sou...

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International conference on Advanced computing and Communication Systems (ICACCS)

作者： John Amose Manimegalai P Pavithra S Susmitha B Ruth S Priyanga S Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Biomedical Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The objective diagnosis of respiratory diseases, by observing pathological sounds is a promising area of research in respiratory health. A GUI-based toolkit to aid a pulmonologist in decision-making is presented in this paper. Age and Gender-specificmachine learning models are trained and analyzed in this work for the classification of wheeze and crackle. Sequence models like LSTM, GRU, and Transformer were implemented, demonstrating high accuracy in classifying intricate lung sound features for diagnosing various respiratory diseases. In this work, MATLAB is used to enable user-friendly visualization, featuring filtered spectrograms and time- domain lung sound signals. In experimental evaluations, age and gender-specific models (child, adult, female) outperformed general models in the classification of wheeze and crackle. GRU and LSTM models achieved 69% accuracy, while the Transformer model outperformed with 750%.

关键词： Analytical models visualization Accuracy Pneumonia Computational modeling Lung Transformers MATLAB Long short term memory Spectrogram

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Explainable AI for Lung Nodule Detection and Classification in CT Images

Explainable AI for Lung Nodule Detection and Classification ...

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conference on Medical Imaging - Computer-Aided Diagnosis

作者： Chang, Chih-Wei Zhao, Qilong Zhao, Liang Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30308 USA Emory Univ Winship Canc Inst Atlanta GA 30308 USA Emory Univ Dept Comp Sci Atlanta GA 30308 USA

ISBN: (纸本)9781510671591;9781510671584

Lung cancer is the second most prevalent and deadliest cancer in the United States, primarily due to its elusive early symptoms that hinder timely diagnosis. Lung nodules, minute anomalous areas, hold potential significance in lung cancer occurrence. Swift identification of these nodules can significantly enhance patient survival rates. Thoracic thin-sliced Computed Tomography (CT) scanning has emerged as a widely adopted approach for radiologists' diagnosing and prognosticating lung abnormalities. However, human factors can be prone to errors stemming from the multifarious causes underlying nodule formation, including factors like pollutants and infections. The domain of deep learning algorithms has recently showcased remarkable prowess in classifying and segmenting medical images. This study is geared towards the creation of a comprehensive framework that seamlessly integrates explainable AI techniques to achieve precise pulmonary nodule detection for computer-aided detection and diagnosis (CAD) systems. The framework's execution is underpinned by an explanation supervision approach, employing radiologists' nodule contours as guiding signals to imbue the model with an understanding of nodule morphologies. The nodule detection is achieved by augmentation of the model's learning capabilities, particularly on smaller datasets. Furthermore, two imputation techniques are deployed to refine the nodule region annotations, thereby attenuating noise inherent in human annotations. These techniques empower the model with steadfast attributions that align with human expectations. Our approach consistently elevates ResNet18's classification performance and explanation quality regarding accuracy and specificity by 22% and 23% using training datasets with 100 samples. For small dataset learning using 50 samples, the proposed framework still outperforms ResNet18 by 16% regarding accuracy and specificity. This framework seamlessly integrates a robust explanation supervision techni

关键词： Lung Computed tomography Lung cancer Artificial intelligence Education and training visualization Cancer Machine learning Magnesium Visual process modeling

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DCP++: A New Registration Method for Brain MRI 2

DCP++: A New Registration Method for Brain MRI

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2nd IEEE International conference on Medical Artificial Intelligence, MedAI 2024

作者： Liao, Yucheng Lei, Bohan Zhuang, Yueting Xu, Xiaoyin Zhang, Min Chu Kochen Honors College Zhejiang University Hangzhou China College of Computer Science & Technology Zhejiang University Hangzhou China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350377613

In geometric computer vision, 3D registration is a critical task for various applications. 3D medical image registration, as one important application of 3D registration, holds substantial clinical potentials for supporting diagnosis, surgeries, and treatments through the registration of images captured at different times or using different modalities. Recently, the rapid advancement and widespread adoption of deep learning have spurred interest in applying this technology to the registration of point cloud data. In this paper, an deep-learning-based 3D brain MRI medical data registration algorithm (DCP++) is constructed. Through employing reasonable loss function, DCP++ achieves efficient and precise registration of 3D brain data. To evaluate the performance of DCP++, we apply the algorithm to the point cloud registration task of 3D human brain MRI images from the ADNI public dataset. Benchmark comparisons, ablation experiment, and visualization analyses are conducted. Experiments demonstrate that compared with state-of-the-art algorithms, DCP++ reduces the registration error by approximately 90%. Tests on real data confirm the accuracy and robustness of the DCP++ algorithm, highlighting its potential in medical practice. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Innovative Solutions for COVID-19 Prevention: Automated Multi-Functional Sanitization Chamber

Innovative Solutions for COVID-19 Prevention: Automated Mult...

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Cognitive computing in Engineering, Communications, Sciences and biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Satendra Pal Singh Tooba Imran Praveen Kumar Mohammad Sarfraz Department of Electrical Engineering ZHCET AMU

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The COVID-19 pandemic has emphasized the critical need for effective health safety measures in public spaces. This paper introduces a sensor-based automatic multifunctional sanitization system designed to automate hand sanitization, body temperature detection, and whole-body disinfection. Utilizing an Arduino microcontroller and various sensors, the system operates seamlessly in high-traffic areas like hospitals, offices, and malls. Key processes include touch-free hand sanitization, temperature screening (<100°F), and sodium hypochlorite fogging for comprehensive disinfection, achieving an average process time of 10–15 seconds per user. The system incorporates audio and visual indicators for completion, ensuring continuous operation without human intervention. Experimental evaluations demonstrate high accuracy in thermal screening (±1°C) and effective disinfection coverage, with minor limitations in sensor performance under extreme environmental conditions. These results highlight the system's efficiency and potential for scalable implementation in public health safety.

关键词： COVID-19 Temperature measurement Hands Temperature sensors visualization Technological innovation biomedical measurement Sensor systems Safety Public healthcare

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Low-Power Approximate computing-based VLSI Architecture for biomedical Signal Processing

Low-Power Approximate Computing-based VLSI Architecture for ...

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

作者： A. Sathiya B. Prabhavathy A Balasupramani Amutha A L. Kavitha R Saravanakumar Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering (Autonomous) Thalavapalayam Karur Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Electronics and communication Engineering Rajalakshmi Institute of Technology Chennai Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan college of Engineering Coimbatore Dept of Electronics Engineering (VLSI) M. Kumarasamy College of Engineering Thalavapalayam Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI architecture which enables biomedical signal processing through energy-efficient digital filtering along with feature extraction as well as pipeline-based clock gating. The proposed architecture combines a distributed arithmetic FIR filter without multipliers to reduce complexity and it integrates a low-power feature extraction system that detects ECG R-peaks and EEG spectral energy and EMG muscle activity effectively. The system uses dynamic clock gating together with power gating to cut down both dynamic power usage and leakage power. The experimental findings show that the developed VLSI architecture both reduces power usage through low hardware complexity while keeping high accuracy levels for biomedical signal processing. The system delivers its performance measurement through power dissipation and area efficiency along with processing speed which enables its use in wearable and implantable medical devices.

关键词： Accuracy Computer architecture Very large scale integration Electrocardiography Feature extraction Electromyography Electroencephalography Real-time systems biomedical communication Clocks

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3D visualization and detection of glomeruli in whole mouse kidney 16

3D visualization and detection of glomeruli in whole mouse k...

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16th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Li, Yuxin Cao, Jia Li, Anan Li, Xiangning Feng, Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi an University of Technology Xi an710048 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China

ISBN: (纸本)9781510667341

The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with nephropathy or circulatory disease. With the development of imaging technology, mesoscopic optical imaging can obtain whole kidney images at single cell resolution. The detection of glomeruli from images is very crucial for understanding the renal function and studying nephropathy. Existing detection methods cannot balance both accuracy and efficiency, so we proposed a deep learning-based glomeruli detection method. First, we imaged an entire mouse kidney with High-Definition fluorescent Micro-Optical Sectioning Tomography (HD-fMOST) and obtained a three-dimensional (3D) kidney image at cellular resolution. Then, we designed an end-To-end 3D convolutional neural network based on the morphological features of glomeruli in the kidney image, which can directly read 3D images and predict the precise coordinates of glomeruli. We used the acquired kidney dataset to train the network and validated the effect of glomeruli detection. Finally, we applied our approach to detect glomeruli in large-scale mouse kidney. The results showed that the proposed method reached the state-of-The-Art level, which is more efficient and accurate compared to similar methods. The proposed method will provide a powerful tool for kidney-related research. © 2023 SPIE.

关键词： Convolutional neural networks

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Low-Power Approximate computing-based VLSI Architecture for biomedical Signal Processing

Low-Power Approximate Computing-based VLSI Architecture for ...

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

作者： Sathiya, A. Prabhavathy, B. Balasupramani, A. Amutha, A. Kavitha, L. Saravanakumar, R. Department of Electronics and Communication Engineering Karur Thalavapalayam639113 India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641105 India Thalavapalayam639113 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

关键词： Medical image processing

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A Comparative Analysis of Multi-Modal Medical Image Fusion Techniques using MSVD, WPD, PCA, and DWT

A Comparative Analysis of Multi-Modal Medical Image Fusion T...

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Cognitive computing in Engineering, Communications, Sciences and biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Image fusion is a technique used in image processing to create a more comprehensive representation by combining features and data from several images. Incorporating medical images from several imaging modalities, such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI), into a single set of data is what multi-modal medical image fusion is all about. Better visualization of anatomical structures and clinical conditions is the outcome of this integration, which increases diagnostic accuracy by using the strengths of each modality. In this paper, MRI, CT, and PET scans are used as experimental modalities. This review aims to compare various multi modal medical image approach based on Multi-resolution Singular Value Decomposition (MSVD), Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD). This paper focuses on exploring latest conventional and non-conventional research being done using these domains. It also compares these methods based on various image quality parameters, and various quantitative checks. Based on this comparison, PCA shows the best results in the comparison as the overall visual and parametric quality of fusion results are better than compared methods.

关键词： visualization Magnetic resonance imaging Computed tomography Wavelet analysis Wavelet packets Discrete wavelet transforms Positron emission tomography Medical diagnostic imaging Image fusion Principal component analysis

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An Effective Security Protocol Design for IRIS based Credential Evaluation using Intensive Deep Learning Scheme 2

An Effective Security Protocol Design for IRIS based Credent...

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2nd IEEE International conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Ramachandran, R. Hema Priya, K. Malathy, K. Sivagamidevi, G. Sudhakar, C. Venkata Indhumathi, V. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India S.A. Engineering College Department of Master of Computer Applications Chennai India Dhanalakshmi College of Engineering Dept of Ai & Ds Chennai India SreeVidyanikethan Engineering College Department of ECE Andhra Pradesh Tirupati India Panimalar Institute of Technology Department of It Chennai India

ISBN: (纸本)9798350315905

The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and does not ensure a high level of security. Authentication using biometric methods can be achieved in several ways. Some of the most advanced and reliable are facial recognition technology and iris recognition. Because it relies heavily on detection of patterns, it is able to reliably identify the rightful owner of an Iris scan. Accuracy as well as effectiveness have both been greatly enhanced in the resultant recognition system. Security breaches and other identification scams are on the rise, making it all the more crucial to implement a robust biometric system. The option that has gained a lot of attention is biometric identification. The iris's potential as a biometric has gained traction in recent years. This quantifiable quality ensures great productivity and precision, which is what triggered the phenomenon. We present a complete ResNet50-based deep learning system for iris identification in this study. Utilizing only a small number of training photos from every class, we train our algorithm on a popular identification of iris dataset, achieving significant gains over prior methods. In addition, we introduce a visualization method that may identify key features of iris pictures that have a significant bearing on the precision of recognition. We anticipate that this approach will be utilized extensively in the future to improve the scalability and precision of various biometrics identification jobs © 2023 IEEE.

关键词： Biometrics

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