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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XII"

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Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based image processing Techniques

Cancer Sight: Illuminating the Hidden-Advancing Breast Cance...

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2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sirisati, Ranga Swamy Kumar, C. Srinivasa Venuthurumilli, Pradeep Ranjith, J. Rao, Kanusu Srinivasa Ghatkesar Hyderabad Telangana Kondapur India Vignan's Institute of Management and Technology for Women Department of Cse Ghatkesar Hyderabad Telangana Kondapur India Mallareddy Engineering College for Women Department of Computer Science & Engineering Maisammagida Hyderabad500090 India Vignan's Institute of Management and Technology for Women Department of Information Technology Ghatkesar Hyderabad Telangana Kondapur India Yogi Vemana University Department of Computer Science and Technology Kadapa India

ISBN: (纸本)9798350313987

Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based image processing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based image processing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based image processing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for mor

关键词： Breast cancer Convolutional neural networks (CNNs) image processing Machine learning Magnetic resonance imaging (MRI) Support vector machines (SVMs)

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Quantifying Stellar Energy Exchange: image Analysis with OpenCV and Machine Learning 5

Quantifying Stellar Energy Exchange: Image Analysis with Ope...

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5th IEEE International conference for Emerging Technology, INCET 2024

作者： Venkteswarulu, B. Kanagala, Chaitanya Snehitha Kakumanu, Naga Venkata Sai Mohana Raghavarapu, Laxmi Narasimha Charan Sai Peravali, Himavath Lakshmi Visveswar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350361155

Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar energy exchange through sophisticated image analysis techniques, harnessing the power of OpenCV and machine learning. Our approach integrates OpenCV's robust image processing capabilities with state-of-the-art machine learning algorithms to automate the intricate task of analyzing astronomical images and predicting energy exchange rates within stellar constellations. The methodology begins with the preprocessing of astronomical images using OpenCV, where radiometric and atmospheric corrections are applied to ensure accurate representations of stellar luminance. Subsequently, features relevant to energy exchange dynamics are extracted from the processed images, employing advanced computer vision techniques. These features encapsulate intricate spatial, spectral, and radiometric characteristics of stars, providing rich representations for subsequent analysis. Machine learning algorithms are then employed to predict the energy exchange rates of stars based on the extracted features. Leveraging the power of convolutional neural networks and ensemble learning techniques, our model learns intricate patterns within the data and delivers accurate estimations of energy exchange dynamics. Through extensive experimentation on real astronomical datasets, our approach demonstrates exceptional performance, achieving precise predictions of energy exchange rates across diverse stellar systems. © 2024 IEEE.

关键词： Spectrum analysis

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks 6th

Image Based Classification of Methods-Time Measurement Opera...

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6th International conference on System-Integrated Intelligence (SysInt)

作者： Rueckert, Patrick Birgy, Katrin Tracht, Kirsten Univ Bremen Bremen Inst Mech Engn BIME Badgasteiner Str 1 D-28359 Bremen Germany

ISBN: (纸本)9783031162817;9783031162800

image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. The five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. The following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.

关键词： Neuronal networks Assembly image processing

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Digital image processing for the Diagnosis of the Cold Inducible Urticaria by Photoplethysmography-Based Methods

Digital image Processing for the Diagnosis of the Cold Induc...

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2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Guryleva, Anastasia Fomina, Daria Volkov, Mikhail Serdoteckova, Sofia Danilycheva, Inna Russian Academy of Sciences Acousto-Optic Spectroscopy Lab Scientific and Technological Center of Unique Instrumentation Moscow Russia Moscow State Scientific and Practical Center for Allergology and Immunology City Clinical Hospital No 52 Ministry of Healthcare of Moscow Russia Sechenov University Department of Clinical Immunology and Allergology Moscow Russia ITMO University Faculty of Physics and Technology Saint-Petersburg Russia NRC Institute of Immunology FMBA of Russia Moscow Russia

ISBN: (纸本)9798350348293

The objectivity and accuracy of the diagnosis of the inducible urticaria can be improved through the analysis of microcirculation parameters. Photoplethysmography is an easily implemented non-invasive method able to collect valuable diagnostic data on the state and function of the vessels. Existing descriptions of photoplethysmography image processing algorithms are limited to the basic principles and general mathematical expressions. At the same time, the algorithm options can vary significantly and affect the data quality. In this paper, we study the optimal characteristics of the algorithms for photoplethysmography-based skin inspection for the diagnosis of the cold inducible urticaria. Presented approach provides automatic, fast and repeatable processing under various input data. © 2023 IEEE.

关键词： image processing

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Building Structures image Matching Using Convolutional Neural Network

Building Structures Image Matching Using Convolutional Neura...

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2024 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2024

作者： Andriyanov, N.A. Magdeev, R.G. Dementyiev, V.E. Financial University under the Government of the Russian Federation Moscow Russia Ulyanovsk State Technical University Ulyanovsk Russia National Research Mordovia State University Saransk Russia

ISBN: (纸本)9798350383614

The paper examines the features of combining multi-temporal and multi-angle images of building structures in order to identify critical changes. It is proposed to carry out such a combination on the basis of high-speed pseudogradient algorithms. The focus of the work is the problem of identifying areas of attention that are the source of combined points. It is proposed to perform such selection using convolutional neural networks of the YOLO family. The consistency of this approach is shown and estimates of the resulting gains are also calculated. It is also suggested to use different optimization methods for improving the processing speed. So the YOLO detector will be very high performance and efficient real time detector. ©2024 IEEE.

关键词： Object detection

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th International conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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Utilising Machine Learning for Modulation and Signal processing 2

Utilising Machine Learning for Modulation and Signal Process...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Singh, Daljeet Pal Solanki, Madhav Singh Jyothi, Renuka S. Maharishi Univ Informat Technol Maharishi Sch Engn & Technol Uttar Pradesh India Sanskriti Univ Dept Comp Sci & Engn Uttar Pradesh India Jain Deemed Be Univ Dept Biotechnol & Genet Sch Sci Bangalore Karnataka India

ISBN: (纸本)9798350395334;9798350395327

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform;waveletbased totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Consisting Communications Modulation Techniques Parameters Management

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Feature identification for parameter extraction and defect detection using machine learning 38

Feature identification for parameter extraction and defect d...

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conference on Metrology, Inspection, and Process Control XXXVIII

作者： Guo, Y. Pahlavani, H. Khachaturiants, A. Elsayed, K. van de Laar, J. Simons, E. Saikumar, N. Sadeghian, H. Nearfield Instruments BV Rotterdam Netherlands

ISBN: (纸本)9781510672178;9781510672161

Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to fine-tune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.

关键词： Scanning Probe Microscopy image processing Parameter extraction Defect detection Machine learning

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