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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing"

共 20880 条记录，以下是4911-4920 订阅

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Breast and Thyroid Cancers Classification using Scaled Conjugation Backpropagation, Levenberg-Marquardt, and Bayesian Regularization

Breast and Thyroid Cancers Classification using Scaled Conju...

引用

artificial Intelligence Robotics, Signal and image processing (AIRoSIP), International conference on

作者： Muhammad Albik Ghalela Yessi Jusman Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

This neural network research is aiming to classify breast cancer and thyroid. The methods used in these experiments are scaled conjugation backpropagation, Levenberg-marquardt, and Bayesian regularization within 10 cross-validation training. The average accuracy of breast cancer classification is 96,92% with scaled conjugation backpropagation, 97,08% with Levenberg-marquadt, and 99,59% for Bayesian regularization. While average accuracy of thyroid classification with 94,18% with scaled conjugation backpropagation, 98,78% with Levenberg-marquardt, and 99,59% with Bayesian regularization. Finally, the result of this study is the differential of each neural network algorithm that can be useful for image medical classification in both breast cancer and thyroid.

关键词： Backpropagation Training Accuracy Thyroid cancer artificial neural networks Breast cancer Bayes methods Thyroid image classification Biomedical imaging

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Classification of MRI's images using the Fractional Fourier Transform and the Local Binary Pattern information by artificial neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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conference on Infrared Remote Sensing and Instrumentation XXIX

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

Pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, neural networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. artificial neural networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the artificial neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： Pattern recognition one-dimensional signature fractional Fourier transform binary rings masks artificial neural Network computer vision image processing local binary pattern neural network

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Gender Classification Based on Fingerprint Using Sobel Filter and artificial neural Network

Gender Classification Based on Fingerprint Using Sobel Filte...

引用

Technology Innovation and Its applications (ICTIIA), International conference on

作者： Sri Suwarno Lukas Chrisantyo Information Technology Faculty Duta Wacana Christian University Yogyakarta Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

Determining gender based on fingerprints is essential in many critical circumstances. Using fingerprints as biometrics is simple and reliable. However, a complex procedure is needed to choose reliable features and appropriate methods to get high-accuracy results. This study aims to estimate gender based on fingerprints using simple features to reduce the need for lengthy computation in subsequent processes. We select a small region of interest (RDI) of the fingerprint to create features. The RDI size is 80% of the image size at the centre. For this RDI selection, we created a custom function with MATLAB. We base our features on Sobel filters and arrange them into histograms. Furthermore, we classify the resulting histogram using a single-layer artificial neural Network (ANN). We tested the model with the Sokoto Coventry Fingerprint Dataset datasets, which comprise 1000 male and 1000 female fingerprints. Despite its simplicity, our model has demonstrated a validation accuracy of up to 68.2% on the dataset, which is lower than the results of more complex Convolutional neural networks (CNN) models. The model accurately predicted males at 71.5% and females at 65.2%.

关键词： Histograms Technological innovation Filters Accuracy Computational modeling Fingerprint recognition Predictive models Mathematical models Convolutional neural networks Reliability

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Medical Diagnostics Using Machine Learning-Nail images

Medical Diagnostics Using Machine Learning-Nail Images

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International conference on Computer and Communications Technologies (ICCCT)

作者： G. Adiline Macriga Vishal. K Harshath. K Department of Information Technology Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

In this study, we explore a novel application of machine learning to revolutionize the field of medical diagnostics. This paper presents an innovative approach to automating the detection and classification of nail diseases using convolutional neural networks (CNNs) and machine learning techniques. By employing an extensive dataset of nail images representing various dermatological conditions, the system demonstrates a high accuracy rate of 92%. Through advanced image pre-processing methods and cutting-edge CNN architectures, we improve diagnostic precision while minimizing the variability that are typically seen in traditional clinical evaluations. The system facilitates faster and more reliable diagnoses, which can significantly enhance patient outcomes and broaden access to dermatological care by addressing the shortcomings of conventional methods. Future research will focus on optimizing the model architecture and developing real-time applications to further elevate its performance and usability in clinical environments.

关键词： image processing Nails Machine learning Computer architecture Real-time systems Convolutional neural networks Medical diagnosis Reliability Usability Diseases

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Deep Multi-Graph Embedded Clustering for Community Detection in FMRI Functional Brain networks Across Individuals

Deep Multi-Graph Embedded Clustering for Community Detection...

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IEEE International conference on image processing

作者： Kai-Jun See Chee-Ming Ting Fuad Noman Junn Yong Loo Yee-Fan Tan Hernando Ombao Raphaël C.-W. Phan School of Information Technology Monash University Malaysia Statistics Program King Abdullah University of Science and Technology (KAUST)

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Analyzing the community structure of brain networks provides new insights into human brain function. Existing studies broadly use conventional network clustering approaches. While graph neural networks have recently shown promise in modeling brain functional connectivity (FC) networks, their applications to brain community detection still need improvement and further refinement. Moreover, identifying common community structure while resolving the single-subject partitions across multiple individual networks remains underexplored. We propose a Deep Multi-Graph Embedded Clustering (DMGEC) framework to identify shared community partition in brain FC networks over a cohort of individuals. By incorporating the consensus information aggregated across network structures, DMGEC leverages a graph autoencoder to produce consensus-aware latent representations of individual networks, and applies deep embedded clustering on the multi-subject network representation to produce common community assignment of brain nodes. Simulations show superior community recovery by our method compared to conventional approaches, especially for networks with large number of communities. When applied to functional magnetic resonance imaging (fMRI) data, the DMGEC achieves outstanding alikeness over individual partitions, and uncovers group-level differences in brain community motifs between major depressive disorder patients and normal controls.

关键词： Representation learning image processing image edge detection Functional magnetic resonance imaging Brain modeling Depression Graph neural networks

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Application of the YOLOv5 neural Network for Pattern Recognition on Radiographic images of the Human Wrist Joint

Application of the YOLOv5 Neural Network for Pattern Recogni...

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International conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Alena S. Krasnoperova Andrey V. Kamenskiy Dept. of Television and Control Laboratory of television automation Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

The possibility of using artificial intelligence in the task of recognizing objects of interest in medical radiographic images has been studied. To implement computer vision, the YOLOv5 neural network and the use of its functionality for pattern recognition on radiographic images of the human wrist joint were considered. YOLOv5 is one of the most popular neural network models due to a number of advantages and features: high learning speed, object detection accuracy, efficient use of hardware and support for various architectures. The use of an artificial neural network in medicine is a promising direction that allows one to obtain data on pathologies and anomalies on radiographs, improving the diagnostic process and providing quick results with a high percentage of reliability. To adapt the model for the task of recognizing objects of the wrist joint in radiographic images, a data set was labeled, including images for retraining the neural network model and test images. Learning key numerical properties is key to building and tuning effective neural network models. The correctness of their selection and debugging have a direct impact on achieving the desired results when retraining and using neural networks. According to the results of the neural network, all classes of objects were correctly identified in more than 70% of the images; this indicator is a worthy result of the work, which confirms the relevance of using a computer vision tool in medical diagnostics and practical application in clinical practice.

关键词： Wrist YOLO Adaptation models image recognition neural networks Bones Pattern recognition Numerical models Object recognition Joints

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4th International conference on artificial Intelligence and Knowledge processing, AIKP 2024

4th International Conference on Artificial Intelligence and ...

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4th International conference on artificial Intelligence and Knowledge processing, AIKP 2024

ISBN: (纸本)9783031734762

The proceedings contain 18 papers. The special focus in this conference is on artificial Intelligence and Knowledge processing. The topics include: Deciphering Excellence in Hospitality: A Study on India's Premier Hotels Using Natural Language processing;Decoding Student Feedback in Engineering Education: A Latent Dirichlet Allocation and PyLDAvis Approach;real Time Traffic Management by Optimizing Level of Service;FGA-Net: Feature-Gated Attention for Glioma Brain Tumor Segmentation in Volumetric MRI images;empowering Breast Cancer Detection: A Novel Hybrid Transfer Learning Approach with Aquila Optimizer;predictive Analytics in Weather Forecasting Using Machine Learning and Deep Learning;Brain Tumor Classification from MRI Scans Using a Novel CNN Architecture and Optimization Techniques;dual Model Dynamics: Enhancing Depression Prediction Through the Integrated Use of Convolutional neural networks and Support Vector Machines in Data-Driven Methods;quantum Simulation of 1D and 2D Lattice-Based Magnetic Quantum Systems Using Heisenberg Model via Variational Quantum Eigensolver;topological Quantum Computing for Engineers;traffic Forecasting with Spatio-Temporal Graph neural networks;A Deep Learning-Based Novel RNN-BiLSTM Architecture for Efficient Threat Detection in IoT Ecosystem;cyber-Attack Detection in Healthcare Systems Based on Modified -Recurrent neural Network Approach;A Comparative Analysis of Aggregation Methods in Federated Learning on MNIST;hybrid Feature Approach for Enhancing Zero-Shot image Classification;abusive Comment Detection from Bengali-English Code-Mixed Social Media Texts Using Ensemble of Deep Learning.

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A new depth residual network combined recurrent with residual structure for human action recognition from videos

A new depth residual network combined recurrent with residua...

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2021 International conference on Computer Graphics, artificial Intelligence, and Data processing, ICCAID 2021

作者： Wang, Min Yi, Yang Tian, Guixiong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangzhou Xinhua University Guangzhou China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Human Action Recognition in videos has attracted much attention due to its wide application prospects in research field, such as human-computer interaction, intelligent detection and video search. And Deep Learning technology has achieved very good results in many fields of the Computer Vision, especially Convolutional networks have achieved the best results in image recognition. However, most Convolutional networks are not suitable for dealing with temporal problems. In response to the problems, this paper proposes a new computational framework for the problem of human action recognition from videos, which is presented in this paper by using modified Deep Residual Network. First, the Deep Residual Network is used to obtain features through the fully connected layer, then the Recurrent neural Network with the Residual Structure is introduced to improve the efficiency of classification. According to the similar characteristics of adjacent frames in content, the Residual Structure is introduced into the RNN to increase the depth of the model and obtain better classification effect. Moreover, in order to solve the over-fitting problem, pruning with preference is carried out. According to the proportion of parameters of each node, some parameters are cut off in the training process. Data experiments on some most popular benchmarks as Olympic Sports and HMDB51 demonstrate that the presented method could achieve comparable good results to the state of the art. © 2022 SPIE.

关键词： Recurrent neural networks

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uPIM: Performance-aware Online Learning Capable processing-in-Memory 3

uPIM: Performance-aware Online Learning Capable Processing-i...

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IEEE 3rd International conference on artificial Intelligence Circuits and Systems (AICAS)

作者： Bavikadi, Sathwika Sutradhar, Purab Ranjan Ganguly, Amlan Dinakarrao, Sai Manoj Pudukotai George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA Rochester Inst Technol Dept Comp Engn Rochester NY 14623 USA

ISBN: (纸本)9781665419130

Machine learning and AI-based automated systems are gaining increasing attention for real-time intelligent applications by virtue of a superior co-ordination between the software and the hardware within these systems. Although the majority of the automated systems are implementing Convolutional neural networks (CNNs), and Deep neural networks (DNNs) on the hardware with impressive accuracy, a significant amount of cost is associated with data movement in these platforms. Recent advancements in processing-in-memory (PIM), a non-von Neumann computing paradigm, have proven to be very effective in minimizing data communication overheads by performing computations within the memory chip. However, these devices are primarily designed as inference engines and therefore have not been adequately investigated for real-time learning capabilities for applications in changing environments. In this work, we introduce uPIM, a PIM architecture that supports a Generative Adversarial Network (GAN)-based performance-aware online learning model for updating the weights with minimal overheads. Our hardware-software co-design approach exhibits superior performance and efficiency in real-time applications like Autonomous Navigation Systems (ANS) by leveraging massive data-level parallelism and ultra-low data movement latency. The evaluations are performed on multiple state-of-the-art deep learning networks like LeNet, AlexNet, ResNet18, 34, 50 on the German Traffic Sign Recognition Benchmark (GTSRB) dataset and the Belgium Traffic Sign Dataset (BTSD) with several data-precisions. The proposed performance-aware, quantization-friendly online learning based PIM architecture achieves an average accuracy of 72% for GTSRB and 83.4% for BTSD dataset under varying environment for CNNs implemented for Traffic Sign Recognition (TSR) with 8-bit fixed point data-precision.

关键词： Performance evaluation neural networks Computer architecture Parallel processing Generative adversarial networks Real-time systems Hardware

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Enhancing Robustness of Implicit neural Representations Against Weight Perturbations

Enhancing Robustness of Implicit Neural Representations Agai...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wenyong Zhou Yuxin Cheng Zhengwu Liu Taiqiang Wu Chen Zhang Ngai Wong Department of EEE The University of Hong Kong Hong Kong SAR

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Implicit neural Representations (INRs) encode discrete signals in a continuous manner using neural networks, demonstrating significant value across various multimedia applications. However, the vulnerability of INRs presents a critical challenge for their real-world deployments, as the network weights might be subjected to unavoidable perturbations. In this work, we investigate the robustness of INRs for the first time and find that even minor perturbations can lead to substantial performance degradation in the quality of signal reconstruction. To mitigate this issue, we formulate the robustness problem in INRs by minimizing the difference between loss with and without weight perturbations. Furthermore, we derive a novel robust loss function to regulate the gradient of the reconstruction loss with respect to weights, thereby enhancing the robustness. Extensive experiments on reconstruction tasks across multiple modalities demonstrate that our method achieves up to a 7.5 dB improvement in peak signal-to-noise ratio (PSNR) values compared to original INRs under noisy conditions.

关键词： PSNR Perturbation methods Noise neural networks Robustness Signal reconstruction Noise measurement Speech processing image reconstruction Gain

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