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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4861-4870 订阅

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Ultrasound-based discrimination of thyroid cancer based on deep learning

Ultrasound-based discrimination of thyroid cancer based on d...

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artificial Intelligence and Electromechanical Automation (AIEA), International conference on

作者： Xi Yang Guangle Zhu School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou Guangdong

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Early diagnosis of benign and malignant thyroid nodules is an important means of detecting thyroid cancer. The classification of thyroid nodule ultrasound images plays a crucial role in the possible detection, diagnosis, and subsequent treatment of thyroid cancer. This study aims to use deep learning models to classify ultrasound images of thyroid nodules and evaluate their potential applications in clinical practice. We collected thyroid ultrasound image data from real patients in hospitals. We attempt to design deep learning models based on Convolutional neural networks (CNN) and transformer like structures for image classification experiments. The experimental results indicate that deep learning models have achieved good results in this task and have certain application prospects. The automatic classification and diagnosis of benign and malignant thyroid nodules through computer deep learning models can accelerate the process of disease diagnosis and treatment for patients. In conclusion, this study provides important theoretical and practical basis for the application of deep learning models in the field of benign and malignant classification of thyroid nodule ultrasound images.

关键词： Deep learning Ultrasonic imaging Thyroid cancer Hospitals Computational modeling Transformers Convolutional neural networks Medical diagnosis Thyroid image classification

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Comparison of MLP and RBF neural Models on the Example Graphical Classification 13

Comparison of MLP and RBF Neural Models on the Example Graph...

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13th International conference on Digital image processing (ICDIP)

作者： Boniecki, P. Zaborowicz, M. Sujak, A. Poznan Univ Life Sci Dept Biosyst Engn Wojska Polskiego 50 PL-60637 Poznan Poland

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

In the paper, the classification capabilities of perceptron and radial neural networks are compared the example process identification of graphical compost quality. The classification was based on graphical information coded as selected quality features of the compost quality, presented in colour digital images. In the paper, MLP (MultiLayer Perceptrons) and RBF (Radial Basis Function) neural classification models are compared, generated using learning sets acquired on the basis of information contained in digital photographs of compost. In order to classify the compost pictures, modern neural modelling methods were used, including digital image analysis techniques. The qualitative analysis of the neural models enabled the compare of the MLP and RBF neuron topology and identification ANN that was characterised by the highest classification capability. Characteristic features enabling effective identification of a pest were 16 selected graphical parameters. The created neuron model is dedicated as a core for computer systems supporting decision processes occurring during compost production.

关键词： artificial neural networks identification of compost quality computer image analysis

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An Explainable Medical Imaging Framework for Modality Classifications Trained Using Small Datasets 1

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21st International conference on image Analysis and processing (ICIAP)

作者： Trenta, Francesca Battiato, Sebastiano Ravi, Daniele Univ Catania IPLAB I-95125 Catania Italy Univ Hertfordshire Hatfield Herts England UCL London England Queen Sq Analyt London England

ISBN: (数字)9783031064272

ISBN: (纸本)9783031064272;9783031064265

With the huge expansion of artificial intelligence in medical imaging, many clinical warehouses, medical centres and research communities, have organized patients' data in well-structured datasets. These datasets are one of the key elements to train AI-enabled solutions. Additionally, the value of such datasets depends on the quality of the underlying data. To maintain the desired high-quality standard, these datasets are actively cleaned and continuously expanded. This labelling process is time-consuming and requires clinical expertise even when a simple classification task must be performed. Therefore, in this work, we propose to tackle this problem by developing a new pipeline for the modality classification of medical images. Our pipeline has the purpose to provide an initial step in organizing a large collection of data and grouping them by modality, thus reducing the involvement of costly human raters. In our experiments, we consider 4 popular deep neural networks as the core engine of the proposed system. The results show that when limited datasets are available simpler pre-trained networks achieved better results than more complex and sophisticated architectures. We demonstrate this by comparing the considered networks on the ADNI dataset and by exploiting explainable AI techniques that help us to understand our hypothesis. Still today, many medical imaging studies make use of limited datasets, therefore we believe that our contribution is particularly relevant to drive future developments of new medical imaging technologies when limited data are available.

关键词： Modality classification Quality control Explainable artificial intelligence

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Enhancing Transportation Infrastructure: A Deep Learning Approach for Pothole Detection 15

Enhancing Transportation Infrastructure: A Deep Learning App...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhatt, Rushin Shah, Jinay Patel, Samir Pandit Deendayal Energy University School of Technology Gujarat Gandhinagar India

ISBN: (纸本)9798350370249

Potholes on roads pose significant challenges to transportation infrastructure, contributing to increased accident risks and substantial maintenance expenses. In response to these issues, this research paper presents a novel approach utilizing deep learning techniques for automated pothole detection on roads. An application of Convolutional neural networks (CNNs) has been proposed to develop a precise pothole detection model capable of analysing still images of roads. Specifically, the YOLOv8 model has been implemented using PyTorch, a powerful deep learning framework, to design and train the pothole detection system. 'You Only Look Once', better known as the YOLO architecture offers efficient object detection on real-time basis, making it suitable for identifying potholes in diverse road conditions. To support model development and evaluation, a comprehensive dataset was leveraged, comprising annotated photos of roads with and without potholes. The efficacy and robustness of the suggested methodology in precisely and resiliently locating potholes are proven through extensive testing and analysis. The trained model exhibits robust performance in detecting potholes of varying sizes and shapes, achieving an average confidence of almost 98% in detecting potholes within images from the validation batch, showcasing its potential for practical deployment in transportation management systems. This research contributes to the advancement of automated infrastructure maintenance practices by harnessing cutting-edge deep learning technologies. The successful implementation of this approach holds promise for improving road safety, optimizing maintenance strategies, and reducing the economic burden associated with pothole-related damages. By showcasing the capabilities of deep learning in pothole detection, this study highlights the transformative potential of artificial intelligence in enhancing transportation infrastructure resilience and sustainability. © 2024 IEEE.

关键词： Deep Learning image processing Pothole Detection

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Enhancing Energy Efficiency and Security in Wireless Sensor networks: A Survey and Proposed Methodology Integrating Prediction Algorithms and Deep neural networks

Enhancing Energy Efficiency and Security in Wireless Sensor ...

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Cybernetics, Cognition and Machine Learning applications (ICCCMLA), IEEE International conference on

作者： Shaik Shafiuddin Konda Hari Krishna Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

This research study provides a complete assessment of energy- and trust-aware techniques in IoT-WSNs, emphasizing the significant problems and limits of current methodologies. Traditional methodologies frequently face challenges such as excessive processing overhead, difficulty reaching real-time optimal solutions, and increased delay, especially in large-scale or resource-constrained contexts. To solve these issues, the paper suggests a new approach that combines powerful prediction algorithms, deep neural networks, and strong security frameworks. The process entails initializing exact inputs and simulation settings, modeling advanced prediction algorithms, developing deep neural networks, and constructing resilient sensor nodes, all while implementing extensive security measures. The suggested approach seeks to considerably improve energy efficiency, network lifetime, and security by focusing on mobile sink movement prediction and network operation optimization. This study not only covers existing methodologies, but also shows the promise of the proposed methodology through extensive analysis and preliminary results, demonstrating its ability to overcome the limits of present WSN deployments.

关键词： Surveys Wireless sensor networks artificial neural networks Predictive models Prediction algorithms Real-time systems Energy efficiency Stability analysis Security Optimization

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Research on fine-grained image description generation method based on deep learning

Research on fine-grained image description generation method...

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IEEE International conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Yechao Wang Wuqiang Shen Chaosheng Yao Xiaosong Su Guangdong Power Grid Corporation Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China Shenzhen E-Link Information Technology Co. Ltd. Shenzhen China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

With the rapid development of deep learning technology, image description generation has emerged as a hot research direction in the field of artificial intelligence, aiming to interpret image content through automatically generated natural language descriptions. Particularly, fine-grained image description generation, which delves into the detailed information within images to provide richer and more precise descriptions, holds broad application prospects, including aiding navigation for the visually impaired, automatic content creation, and augmented reality, among others. This study explores the technology of fine-grained image description generation, proposing a deep learning model that integrates Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs) to enhance the quality and accuracy of image descriptions. We employed a composite loss function to refine the model’s training process and introduced an attention mechanism to better capture key information in images. Through experimental validation on multiple public datasets, our model demonstrated superior performance to existing technologies. Additionally, we analyzed key factors affecting model performance and discussed potential directions for future improvements. The outcomes of this study not only provide an effective method for fine-grained image description generation but also pave new pathways for the application of deep learning technology in image understanding and natural language processing.

关键词： Deep learning Training Attention mechanisms Recurrent neural networks Accuracy Navigation Computational modeling Safety Convolutional neural networks Standards

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Enhancing Small Dataset Classification Using Projected Quantum Kernels with Convolutional neural networks

Enhancing Small Dataset Classification Using Projected Quant...

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artificial Intelligence (SLAAI-ICAI), SLAAI International conference on

作者： A.M.A.S.D. Alagiyawanna Asoka Karunananda A. Mahasinghe Thushari Silva Department of Computational Mathematics University of Moratuwa Sri Lanka Department of Mathematics University of Colombo Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

Convolutional neural networks (CNNs) have shown promising results in efficiency and accuracy in image classification. However, their efficacy often relies on large, labeled datasets, posing challenges for applications with limited data availability. Our research addresses these challenges by introducing an innovative approach that leverages projected quantum kernels (PQK) to enhance feature extraction for CNNs, specifically tailored for small datasets. Projected quantum kernels, derived from quantum computing principles, offer a promising avenue for capturing complex patterns and intricate data structures that traditional CNNs might miss. By incorporating these kernels into the feature extraction process, we improved the representational ability of CNNs. Our experiments demonstrated that, with 1000 training samples, the PQK-enhanced CNN achieved 95% accuracy on the MNIST dataset and 90% on the CIFAR-10 dataset, significantly outperforming the classical CNN, which achieved only 60% and 12% accuracy on the respective datasets. This research reveals the potential of quantum computing in overcoming data scarcity issues in machine learning and paves the way for future exploration of quantum-assisted neural networks, suggesting that projected quantum kernels can serve as a powerful approach for enhancing CNN-based classification in data-constrained environments.

关键词： Training Accuracy Quantum computing Computational modeling neural networks Feature extraction Data models Convolutional neural networks Kernel Testing

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22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing

22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RI...

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2023 IEEE International Solid-State Circuits conference, ISSCC 2023

作者： Conti, Francesco Rossi, Davide Paulin, Gianna Garofalo, Anaelo Di Mauro, Alfio Rutishauer, Georg Ottavi, Gian Marco Eggimann, Manuel Okuhara, Hayate Huard, Vincent Montfort, Olivier Jure, Lionel Exibard, Nils Gouedo, Pascal Louvat, Mathieu Botte, Emmanuel Benini, Luca University of Bologna Bologna Italy Eth Zürich Zürich Switzerland Dolphin Design Meylan France

ISBN: (纸本)9781665428002

Emerging artificial Intelligence-enabled Internet-of-Things (Al-loT) SoCs [1-4] for augmented reality, personalized healthcare and nano-robotics need to run a large variety of tasks within a power envelope of a few tens of mW: compute-intensive but bit-precision-tolerant Deep neural networks (DNNs), as well as signal processing and control requiring high-precision floating-point. Performance and energy constraints vary greatly between different applications and even within different stages of the same application. We present Marsellus (Fig. 22.1.1), an all-digital Al-loT end-node heterogeneous mathsfSoC fabricated in GlobalFoundries 22mathsfnm FDX that combines three key contributions to enable aggressive scaling of performance and energy: 1) a generalpurpose cluster of 16 RISC-V DSP cores attuned for execution of a diverse range of workloads exploiting 4mathsfb and 2mathsfb arithmetic extensions (XpulpNN), combined with fused MAC & LOAD (M&L) operations and floating-point support;2) a 2-8b reconfigurable binary engine to accelerate 3times 3 and 1times 1 (pointwise) convolutions in DNNs;3) a set of On-Chip Monitoring (OCM) blocks connected to an Adaptive Body Bias (ABB) generator and a hardware control loop, enabling on-the-fly adaptation of transistor threshold voltages. © 2023 IEEE.

关键词： Deep neural networks

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ComPRePS: An Automated Cloud-based image Analysis tool to democratize AI in Digital Pathology

ComPRePS: An Automated Cloud-based Image Analysis tool to de...

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conference on Medical Imaging - Digital and Computational Pathology

作者： Mimar, Sayat Paul, Anindya S. Lucarelli, Nicholas Border, Samuel Naglah, Ahmed Barisoni, Laura Hodgin, Jeffrey Rosenberg, Avi Z. Clapp, William Sarder, Pinaki Univ Florida Quantitat Hlth Sect Div Nephrol Hypertens & Renal Transplantat Dept Med Gainesville FL 32611 USA Duke Univ Dept Pathol Div & AI Computat Pathol Durham NC USA Univ Michigan Dept Pathol Ann Arbor MI USA Johns Hopkins Univ Dept Pathol Baltimore MD USA Univ Florida Coll Med Dept Pathol Immunol & Lab Med Gainesville FL USA

ISBN: (纸本)9781510671713;9781510671706

artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles(1) with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

关键词： AI FAIR Digital and Computational Pathology Whole slide images Functional tissue units image segmentation Automatic feature extraction AI in healthcare Cloud platform

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BENCHMARKING DEEP LEARNING INFERENCE OF REMOTE SENSING imageRY ON THE QUALCOMM SNAPDRAGON AND INTEL MOVIDIUS MYRIAD X PROCESSORS ONBOARD THE INTERNATIONAL SPACE STATION

BENCHMARKING DEEP LEARNING INFERENCE OF REMOTE SENSING IMAGE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Dunkel, Emily Swope, Jason Towfic, Zaid Chien, Steve Russell, Damon Sauvageau, Joseph Sheldon, Douglas Romero-Canas, Juan Luis Espinosa-Aranda, Jose Buckley, Leonie Hervas-Martin, Elena Fernandez, Mark Knox, Carrie CALTECH Jet Prop Lab Pasadena CA 91125 USA Ubotica Ciudad Real Spain Hewlett Packard Enterprise Houston TX USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Deep space missions can benefit from onboard image analysis. We demonstrate deep learning inference to facilitate such analysis for future mission adoption. Traditional space flight hardware provides modest compute when compared to today's laptop and desktop computers. New generations of commercial off the shelf (COTS) processors designed for embedded applications, such as the Qualcomm Snapdragon and Movidius Myriad X, deliver significant compute in small Size Weight and Power (SWaP) packaging and offer direct hardware acceleration for deep neural networks. We deploy neural network models on these processors hosted by Hewlett Packard Enterprise's Spaceborne Computer-2 onboard the International Space Station (ISS). We benchmark a variety of algorithms trained on imagery from Earth or Mars, as well as some standard deep learning models for image classification.

关键词： Deep Learning Edge processing Space applications Machine Learning artificial Intelligence COTS embedded processors

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