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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81-90 订阅

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Exploring Machine Learning Approaches for Future Disease Prediction in Gynecology: An Experimental Study

Exploring Machine Learning Approaches for Future Disease Pre...

引用

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

作者： Enes Bajrami Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje North Macedonia

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

This study delves into the potential of machine learning in the early detection of cervical cancer, a condition that remains one of the most significant health burdens in resource-limited settings, where delays and inaccuracies in diagnostics frequently lead to adverse outcomes. Utilizing a dataset of 858 patients treated at the Universitario de Caracas Hospital, the research evaluates various supervised learning models, including Support Vector Machines, Decision Trees, and Random Forests, to predict risk factor classifications in cervical cancer. The methodology incorporates data preprocessing, feature selection, and cross-validation using the caret package to ensure the results are robust, reliable, and reproducible. Among the models tested, Random Forest demonstrated superior performance, achieving the highest scores in accuracy, precision, and recall, making it the most effective tool in this study. Critical risk factors such as age, lifestyle, and medical history were highlighted through visual tools like feature importance heat maps and confusion matrices, emphasizing their role in predictive accuracy. This research underscores the pressing need for innovative technological solutions that assist healthcare professionals in diagnostic and treatment planning processes, particularly in under-resourced areas. By leveraging machine learning, these diagnostic gaps can be bridged, enabling timely interventions and significantly improving patient outcomes. Moreover, the findings illuminate the broader transformative potential of machine learning, not only in gynecological health but also in expanding predictive diagnostics to other medical domains, thereby revolutionizing healthcare systems on a global scale.

关键词： Support vector machines visualization Accuracy Gynecology Predictive models Reliability History Cervical cancer Random forests Medical diagnostic imaging

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VenoScan: AI-Powered Classification of Venomous and Non-Venomous Snakes

VenoScan: AI-Powered Classification of Venomous and Non-Veno...

引用

International Symposium on Advanced computing and Communication (ISACC)

作者： Ch. Rajendra Prasad Srinivas Samala Ramu Moola Aggadi Meghana Bongu Akshaya Madipeddi Sai Harshitha Dept. of ECE SR University Warangal India Dept. of ECE Sumathi Reddy Institute of Technology for Women Warangal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Understanding the systems of snakes, particularly poisonous ones, can be challenging for novices. Thus, snake species recognition assists the medical field. Medical professionals identify and treat dangerous snakebites. Traditional verification methods need professional expertise, which may be unavailable in distant places. VenoScan, an AI-based online software, addresses this issue. Doctors can classify snakes using image analysis, and the forest department has it. This work classified poisonous and non-venomous snakes using modified DensseNet121 pre-trained model. This model uses Kaggle Snake Dataset-India. The DenseNet model showed promise with 98.73% test accuracy and 94.95% validation accuracy. Model is deployed on Visual Studio-Developed web app. This website helps doctors identify and treat patients to save lives.

关键词： Training visualization Accuracy Computational modeling Pipelines Medical services Software Intelligent systems biomedical imaging Overfitting

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Enhanced Super-Resolution with EDSR: Optimizing Image Quality across Multiple Scaling Factors

Enhanced Super-Resolution with EDSR: Optimizing Image Qualit...

引用

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

作者： Anika Kirana Venkat Keerti Kulkarni Dept. of Electronics and Communication. Engineering BNM Institute of Technology Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

In today’s world where high resolution visual content is preferred in domains like medical imaging, satellite imagery, and digital entertainment, effective image super resolution is crucial for success. This paper focuses on not just upscaling the resolution but restoring the lost details to improve the overall quality of images. The enhanced deep residual network (EDSR) is utilized to significantly improve image resolution and reduce blurring and outperforming traditional methods. While EDSR does not eliminate all forms of quality degradation, it effectively improves image sharpness, clarity and balancing computational efficiency with enhanced detail construction. The model’s performance is elevated using traditional metrics like PSNR and SSIM to quantify the quality enhancement. In the end comparisons are drawn across the scaling factors of 2x, 3x and 4x to show the models ability to perform across various domains. Here, the approach was evaluated across various scaling factors of 2x, 3x, and 4x showing consistent improvement in image clarity, sharpness and structural integrity and surpassing traditional interpolation methods.

关键词： Image quality visualization PSNR Computational modeling Superresolution Satellite images Image restoration Computational efficiency Residual neural networks biomedical imaging

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Enhancing Secure Medical Image Transmission Using Visually Meaningful Medical Image Encryption

Enhancing Secure Medical Image Transmission Using Visually M...

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Computational Intelligence and Network Systems (CINS), International conference on

作者： Rajamohan Jayabal S. Priya Sahajanya S C. Manikandan School of Electrical and Electronics Engineering SASTRA Deemed to be University Thanjavur TamilNadu India School of computing SASTRA Deemed to be University Thanjavur TamilNadu India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

Medical images are transmitted to the remote location in a secure manner using medical image encryption. Conventional medical image encryption generates an unfamiliar image format that looks like noise or texture structure, which shows that the encrypted medical image contains secret information. Hence, the attacker aware of the visual existence of the secret image. To safeguard the visual appearance of the encrypted image, this paper proposed a visually meaningful medical image encryption based on Substitution and Permutation Network (SPN) structure. Performance analysis shows that the proposed methodology performs exceptionally well with various performance measures are considered.

关键词： Measurement visualization Image communication Noise Transforms Encryption Performance analysis Cryptography biomedical imaging Network systems

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Structural Homology of Epitope Binding Mimicry in the Onset of Type 1 Diabetes Mellitus

Structural Homology of Epitope Binding Mimicry in the Onset ...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gardner, Ryan Wilkins, Joshua Mistry, Sejal Gouripeddi, Ramkiran Facelli, Julio C. Weber State University OgdenUT United States North Carolina a&t GreensboroNC United States University of Utah Department of Biomedical Informatics Salt Lake CityUT United States University of Utah Department of Biomedical Informatics Utah Clinical and Translational Science Institute Salt Lake CityUT United States

ISBN: (纸本)9798350337488

Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (EINF) and T1DM epitopes (ET1D) that demonstrated sequence homology;however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potentials of 35 pairs of epitopes identified in previous work from our laboratory. For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold and I-TASSER software programs. We considered a structural match of EINF and ET1D pairs successful if the RMSD was INF/ET1D structurally unmatched pairs. Despite structural differences, these four EINF/ET1D pairs show similar electrostatic distributions, indicating that they may still bind to the same protein targets, major histocompatibility complex molecules, for T1DM. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for further study. © 2023 IEEE.

关键词： Islet Autoimmunity Molecular Mimicry Protein Structure Prediction Type 1 Diabetes Mellitus

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Parsed Region Feature Extraction Method for Hepatocellular Carcinoma Detection Using Medical Images

Parsed Region Feature Extraction Method for Hepatocellular C...

引用

Smart Technologies and Systems for Next Generation computing (ICSTSN), International conference on

作者： T Thangam A. Kaleel Rahuman J. G. Sabarish S. Sudhakar P. Jeyalakshmi Department of ECE PSNA College of Engineering and Technology Dindigul India Department of ECE NPR College Of Engineering and Technology Dindigul India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Hepatocellular carcinoma detection using medical images relies on precise feature selection from the set of extracted ones that augments high detection accuracy. This article introduces a Parsed Region Feature Extraction Method (PRFEM) to improve the carcinoma detection accuracy from medial input images. The proposed method utilizes a two-layer convoluted neural network with a linear pooling function to select optimal features for detection. The first layer is responsible for selecting visual pattern-based features that improve the region detection. In the second layer, the identified features are verified for their missing distribution sequences that are useful in retaining precision. The linear pooling identifies the distribution break points to train the CNN with the need for high feature selection. This improves the detection precision with minimum errors. The proposed PRFEM improves detection precision, feature selection, and accuracy by 11.7%, 9.4%, and 10.5% respectively for the varying patterns.

关键词： visualization Accuracy Neural networks Feature extraction Next generation networking biomedical imaging

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Diffusion Model with Self-supervised Training for Subject-aware Photoplethysmography Recovery

Diffusion Model with Self-supervised Training for Subject-aw...

引用

IEEE biomedical Circuits and Systems (BIOCAS)

作者： Daomiao Wang Qihan Hu Xujian Feng Cuiwei Yang Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Photoplethysmography (PPG) has emerged as a promising technology for wearable personal healthcare due to its non-invasive and cost-effective nature. However, owing to the vulnerability to various noise, the useful PPG signals are few and the usability of PPG-based systems are limited. In this paper, we address the challenge of recovering extremely corrupted PPG waveforms with severe distortions, which lead to missing data. Specifically, we propose a novel diffusion-based framework that incorporates remaining values and subject-aware embedding, along with a self-supervised generative training strategy. Extensive experiments were performed on two datasets under three missing rates and two missing patterns. Our proposed model achieves significant improvements of nearly 100% over existing imputation models on quantitative metrics, and together with the visualization, demonstrating its effectiveness for data distribution generation and corrupted PPG restoration. Our proposed method also provides a new perspective for PPG denoising.

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Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

Algorithm Research of ELMo Word Embedding and Deep Learning ...

引用

IEEE International conference on Power, Intelligent computing and Systems (ICPICS)

作者： Xiaohan Cheng Taiyuan Mei Yun Zi Qi Wang Zijun Gao Haowei Yang Northeastern University Boston USA Georgia Institute of Technology Atlanta USA University of Houston Houston USA

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images as input, and the feature extraction process does not consider semantic information. This project intends to take ELMo-MCT as the main task and obtain multiple visual features related to the original image through self-attention mechanism. In this paper, a large number of experiments are carried out on three zero-shot learning reference datasets, and the best harmonic average accuracy is obtained compared with the most advanced algorithms.

关键词： visualization Image recognition Accuracy Semantics Zero shot learning Feature extraction Transformers Vectors biomedical imaging Testing

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Wearable Pain Detector Using Heart Rate and Skin Conductance

Wearable Pain Detector Using Heart Rate and Skin Conductance

引用

computing (ICOCO), IEEE International conference on

作者： Fadli Maulana Bagas Daffa Torrie P.H Granatha Anugrah Wahmisari Priharti Hesty Susanti School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Pain not only impacts the physical condition but is also influenced by physiological factors that play a role in amplifying or influencing pain. A common approach used to manage pain is the use of opioids. The use of opioids in pain management has risks, especially overdose due to inappropriate use. One factor that can contribute to overdose is the lack of objective pain assessment. One method to assess pain is the visual analog scale. Due to its subjective nature, this can lead to inconsistent and inaccurate assessments. To overcome this impact, a wearable pain detector was developed that is designed to detect pain by combining two physiological parameters, namely heart rate activity and skin conductance, which produces a pain scale with a range of 0-10, and provides classification with categories ranging from “no pain” to “most severe pain”. The classification process is performed using the simple linear regression method. Overall, the wearable pain detector had an accuracy of 75% in calculating the pain scale of the subject. The product is designed in the form of a wearable device that resembles a watch, making it comfortable for patients to use.

关键词： Heart rate visualization Accuracy Pain Detectors Opioids Skin biomedical monitoring Wearable devices Monitoring

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Survey of Large Language Models for Answering Questions Across Various Fields

Survey of Large Language Models for Answering Questions Acro...

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International conference on Advanced computing and Communication Systems (ICACCS)

作者： Pasi Shailendra Rudra Chandra Ghosh Rajdeep Kumar Nitin Sharma Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In recent years, the burgeoning popularity of language models stems from their demonstrably superior performance compared to traditional machine learning techniques and conventional deep learning models across various Natural Language Processing (NLP) tasks. Despite their remarkable achievements, large language models (LLMs) are not immune to constraints, such as sporadic generation of inaccurate responses, particularly noticeable in intricate queries. Presently, LLMs find extensive application across diverse domains, with one prominent example being question-answering tasks, wherein the model is tasked with generating responses based on provided input questions and context. The intricacy escalates when the model operates in a context-agnostic manner. This study presents a thorough examination of question-answering tasks across different domains, including Open domain, Medical domain, Visual, and Multi-lingual question-answering. We delve into the latest advancements in LLMs across these domains, offering nuanced insights into their efficacy and limitations.

关键词： Surveys visualization Large language models Computational modeling Oral communication Question answering (information retrieval) Tuning Context modeling biomedical imaging Immune system

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