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检索条件"任意字段=10th International Conference on Computer Analysis of Images and Patterns"

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Ten Years of Research on Cardiac Imaging in Diagnosing Heart Diseases 15

Ten Years of Research on Cardiac Imaging in Diagnosing Heart...

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaithra, C.S. Siddesha, S. Niranjan, S.K. Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9798331300579

Cardiac images play a vital role in early detection and diagnosing the heart diseases. Due to the advancement in machine learning techniques many tools are emerging for early detection of heart disease and its diagnosis. Image based diagnosis requires previous clinical data which is extracted from hidden image patterns. Machine learning algorithms with cardiac images gaining prominence in healthcare by automating calculations and finding new patterns in data and provide accurate results for diagnosing patients in early stages. this work provides the detailed 10 years of studies on different cardiac images for diagnosing the heart diseases using various machine learning methods. © Grenze Scientific Society, 2024.

关键词： Magnetic resonance imaging

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Fuzzy inference system for detection CT and X-Ray image's edges 18

Fuzzy inference system for detection CT and X-Ray image's ed...

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18th IEEE international conference on Application of Information and Communication Technologies

作者： Jabbar, Shaima Ibraheem Aladi, Abathar Qahtan Omran Al Furat Al Awsat Tech Babylon Iraq Mirjn Teaching Hosp Babylon Iraq

ISBN: (纸本)9798350387544;9798350387537

Medical imaging, including X-ray and Computed Tomography (CT) images, plays a crucial role in aiding doctors with the diagnosis and monitoring of diseases Highlighting significant details in these images can greatly assist doctors in making accurate diagnoses. this research proposes a novel and rapid technique based on a fuzzy inference system to extract details represented by the edges of the images. the proposed technique involves three steps: fuzzification, applying fuzzy rules, and defuzzification. It was tested on 60 image samples, consisting of 30 X-ray images and 30 CT images of the lung area. the results obtained from processing the CT images and X-ray images were compared, along with a comparative analysis between the two types of images. the proposed method showed a difference of 31.38% for dental X-ray images and 43.6% for CT images when compared to the traditional method (Sobel edge detection). this assessment was based on the quantitative evaluation of the results using the F-scale. Due to the difference in texture patterns between X-ray and CT images, there was a slight variation in the evaluations, approximately 10%.

关键词： fuzzy inference system edge detection Computed Tomography (CT) images X-ray images COVID-19

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Faux Reality Detector 10

Faux Reality Detector

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shakthi Kiran, R. Sugumar, A. Vishnuprabhu, R. Sanjeeva Kumar, M. Sreemathy, J. Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

With the advent of a new era in digital content creation, deep learning and generative models have completely changed the way we interact with multimedia. Strong generative adversarial networks (GANs) have been developed, but this has led to grave worries about the potential for malevolent usage. Deep fakes, which are produced by GAN and are hyper-realistic digital forgeries that might be used to spread incorrect information and fool people, pose a serious threat to civilization. As a result, developing reliable and effective deep fake detection algorithms has become an important study area. the major goal of this project is to create and deploy a deep fake detection system that can accurately identify between authentic and false images. Such fake faces can be recognized using a deep learning architecture based on CNNs (Convolutional Neural Networks). Such fake faces can be identified with practically perfect accuracy using a deep learning architecture based on CNNs (Convolutional Neural Networks). based on the fact that the textures of real faces and synthetic ones are very different. the comprehensive research and analysis carried out to understand the fundamental concepts underlying deep fake generation and detection are described in this paper. © 2024 IEEE.

关键词： Generative adversarial networks

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analysis of Image Enhancement and Deep Learning Techniques 10

Analysis of Image Enhancement and Deep Learning Techniques

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumari, Sudesh Kumar, Karan Dept. of Electronics and Communication Engineering Ambala India Bpsmv Sonipat India

ISBN: (纸本)9789380544472

Image analysis is a basic step of transforming an image by extracting meaningful information from digitally stored images through certain functions to improve the quality of image for further applications. these transformations include feature extraction, edge detection, segmentation, filtering, compression, recognition, denoising, image inpainting, and pixel-level enhancement, etc. In this paper, some standard methods of image enhancement are discussed and compared with deep learning techniques, which are the recent areas of research in the field of image processing as well as in computer vision and medical image processing. the paper provides a comprehensive overview of standard image processing techniques and deep learning-based techniques along with their merits and demerits. Deep learning techniques have some exclusive features like less requirement of pre-processing of data, high-dimensional feature extraction, improved accuracy, and better image quality as compared to standard image processing techniques. As deep learning techniques are commonly used as image generation techniques and performing well in medical imaging, character generation, text to speech generation and helpful in identifying fake data. these techniques can improve the creativity by generating new images for interior designing of homes and office spaces as well as the designing of fabrics, automobiles. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Image enhancement

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Anomaly Detection in Retinal Imagery Using Variational Autoencoders 24

Anomaly Detection in Retinal Imagery Using Variational Autoe...

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7th international conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Lebaka, Kagiso Blessing Sithungu, Siphesihle Philezwini Academy of Computer Science and Software Engineering University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

this paper situates itself within the broader medical imaging field, focusing on applying generative modelling techniques for anomaly detection in retinal images. Given the complexity of retinal structures, detecting subtle abnormalities that signal disease requires models that can learn intricate patterns and discern deviations. this study explores the use of a Variational Autoencoder (VAE) to reconstruct healthy retinal images and identify anomalies through reconstruction loss and latent space analysis. With the use of a dataset with an imbalance of healthy and abnormal samples, the model was trained exclusively on healthy images to capture a baseline of normal retinal features. the findings suggest that while the VAE can effectively reconstruct broader structural patterns, it needs to work on finer details, impacting its sensitivity in anomaly detection. these results highlight the challenges inherent in applying generative models to medical diagnostics and emphasise the need for advanced techniques, such as contrastive learning, that may enhance feature extraction. thus, this paper contributes to the ongoing discourse on the suitability of generative models for medical imaging and supports continued research into more nuanced methods to meet the specific demands of retinal anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： Ophthalmology

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Systematic Review on Interpretability in computer-Aided Alcohol Use Disorder Diagnosis 10

Systematic Review on Interpretability in Computer-Aided Alco...

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10th international conference on Wireless and Telematics, ICWT 2024

作者： Janah, Nur Zahrati Permanasari, Adhistya Erna Setiawan, Noor Akhmad Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350377057

Numerous studies have investigated the detection of AUD using EEG signals. However, the real-world implementation of diagnostic systems is hindered by users' difficulty in understanding the rationale behind a decision. this study presents a systematic literature review based on PRISMA guidelines, utilizing journal and conference articles from 2019 to 2023 obtained from Scopus. Among 66 collected studies, feature engineering techniques dominated by EEG sub band decomposition processes, followed by time-domain feature extraction, EEG conversion to images, and brain connectivity features. With advancements in computation, comprehensive hybrid features and raw EEG data are emerging as inputs for classifiers. Some studies also use dimensionality reduction techniques such as statistical analysis, optimization methods, feature reduction methods, and manual selection. Moreover, while black box classifier models are widely used, intrinsically interpretable classifiers exhibit no significant differences in accuracy performance. Future work aims to develop AUD detection systems with enhanced interpretability while maintaining robust classification performance, utilizing features informed by experts' domain knowledge to facilitate decision explanation. © 2024 IEEE.

关键词： Dimensionality reduction

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Lung Cancer Detection from CT images Using Image Processing and Machine Learning Techniques 10

Lung Cancer Detection from CT Images Using Image Processing ...

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10th international conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Sawalha, Deema Sohrab Salman, Adnan An-najah National University Computational Science Program Nablus Palestine An-najah National University Computer Science Department Nablus Palestine

ISBN: (纸本)9798350304299

Lung cancer is the most common type of cancer among males worldwide. It accounts for one of every five cancer-related fatalities and is prevalent in people aged 55 to 65. Detecting lung cancer in its earliest stages is a crucial step in the treatment process that can significantly increase the chance of survival. In this paper, we used image processing techniques with MATLAB on computed tomography (CT) images of lung cancer for multiple patients to determine the location and extent of cancerous spots. the stages included image analysis and segmentation, feature extraction, and candidate identification as distinct regions of interest (ROI). Algorithms based on machine learning were utilized to classify cancer from the ROIs of candidates by extracting the characteristics required for the classification of pathologic features from the annotated ROIs. Comparing the evaluated algorithms in order to identify the optimal algorithm for cancer detection. © 2023 IEEE.

关键词： computerized tomography

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A Machine Learning Model for Prediction of Malaria from Microscopic Blood Cell images 17

A Machine Learning Model for Prediction of Malaria from Micr...

引用

IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of things, ithings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Ooko, Samson Otieno Kagwi, Charles theuri Adventist University of Africa School of Postgraduate Studies Nairobi Kenya University of Eastern Africa Baraton Information Systems and Computing

ISBN: (纸本)9798350351637

Machine learning (ML), a pivotal subset of artificial intelligence (AI), has been instrumental in developing systems that learn from and enhance their performance based on data. this technology has facilitated the creation of predictive models for a range of diseases, including pneumonia, heart disease, and COVID-19, allowing for diagnosis based on specific data parameters. Traditionally, malaria detection has relied on the microscopic examination of blood cells, a method that is not only time-consuming but also prone to misdiagnosis, leading potentially to the incorrect treatment of patients. this study introduces a machine-learning model designed to identify patterns in microscopic images of blood cells to differentiate between healthy and malaria-infected cells. Utilizing publicly available datasets from Kaggle for both training and testing, we evaluated the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) algorithms. Our findings show that the CNN model significantly outperformed the KNN model, achieving an accuracy of 94%, and requiring less memory and computational time. therefore, the CNN model is recommended for further development and use. the implementation of this model represents a significant step forward in achieving the sustainable development goals related to health and wellness, offering a more reliable, efficient, and scalable tool for malaria diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Behavior analysis for Potential Guide Dogs 10

Behavior Analysis for Potential Guide Dogs

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10th international conference on Animal-computer Interaction (ACI)

作者： Wu, Yifan NC State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9798400716560

Behavior analysis serves an important role in guide dog training, where guide dog experts evaluate each potential guide dog and make decisions based on their behaviors under various environments. However, a lack of objective measures to comprehensively evaluate each candidate has become a rising challenge for guide dog organizations. In partnership with Guiding Eyes for the Blind (GEB), a non- profit guide dog organization in the United States, my research focuses on using data analysis and machine learning methods to objectively analyze the behaviors of potential guide dogs using accelerometry data collected using an inertial measurement unit (IMU) under various application scenarios. By conducting two parallel experiments to analyze different aspects of behavior and movement features, the preliminary results indicate that behavior patterns could be extracted to help identify potential guide dogs.

关键词： Guide Dog Canine Behavior analysis IMU Machine Learning

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Advancing Optical Tomography Image analysis: Exploring Convolutional Neural Network Model Variants for Retinal Damage Detection 10

Advancing Optical Tomography Image Analysis: Exploring Convo...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Sathyan, S. Chanchal, M. Raghul, S. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382471

Retina is a vital component of the eye;it plays a crucial role in visual perception. Any damage to the retina can lead to vision impairment and severe ocular diseases. Early detection and accurate diagnosis of retinal conditions is crucial. In the domain of medical image analysis, especially within the scope of retinal imaging, convolutional neural network (CNN)-based deep learning models have exhibited promising results. this paper involves the detection of retinal conditions such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and Drusen. the paper presents a comparative study of CNN models for the detection of these retinal damages using Optical Coherence Tomography (OCT) images. this study focuses on evaluating the performance of popular CNN architectures, like Residual Networks (ResNet), Dense Convolutional Networks (DenseNet), Inception Networks, Visual Geometry Group (VGG). Also, the paper provides a comparative analysis of different depth variants of deep learning models such as ResNet (18, 50 and 101), DenseNet (121, 169, and 201), Inception-V3, Inception-ResNet-V2, and VGG (16 and 19). these models are assessed on a dataset containing OCT images of Normal, DME, CNV and Drusen retinal conditions. the effectiveness of the models are evaluated using evaluation metrics such as accuracy, precision, recall and F1 score. Additionally, visualizations such as confusion matrix, training accuracy versus validation accuracy plots, training loss versus validation loss plots are utilized to provide a better understanding of the model performance. the idea is to explore the impact of depth on the model performance and understand how deeper models demonstrate an enhanced ability to learn complex features and capture intricate details within the retinal data. Exploring these depth variants aims to provide insights into the advantages and potential trade-offs associated with increasing depth of deep learning models for retinal damage detection tasks. © 2

关键词： choroidal neovascularization deep learning DenseNet diabetic macular edema drusen Inception networks medical image analysis ocular diseases optical coherence tomography (OCT) ResNet retina retinal imaging VGG

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