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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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Domain Transfer learning for Medical image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. This era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. This approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. The most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Diagnosis of Bone Fracture Based on Deep learning networks

Diagnosis of Bone Fracture Based on Deep Learning Networks

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International conference on Advances and applications of Artificial Intelligence and machine learning, ICAAAIML 2023

作者： Jaspin, K. Kiruba, Mercy Priyadharshini St. Joseph’s Institute of Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819795062

Bone fractures are a common and frequently serious medical problem for which the precise diagnosis is essential to providing the best possible care. The suggested method makes use of deep learning (DL) to automate the fracture identification process from medical imaging data, including X-rays. A popular family of deep learning models for image analysis applications is called convolutional neural networks, or CNNs. Their effectiveness is in their ability to extract features and categorize them. Numerous architectures are employed for analysis, including pretrained models like GoogLeNet and MobileNet, as well as a manual fracture detection model called ManualNet are implemented. Preparing the data, designing the model architecture, training, and evaluating the process are the main processes in fracture detection. For both healthy and shattered bones, the best model's accuracy is 95%. Additionally, this system is able to be incorporated into hospital workflows to deliver quick and automated early fracture examinations, lessening the workload of radiologists and facilitating quicker patient care. Its real-time implementation could lead to better patient outcomes and lower medical expenses. This system advances the field of medical image analysis and demonstrates how deep learning may be used practically to diagnose bone fractures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Enhancing the Compressed Video Quality through a neural Network Approach 4

Enhancing the Compressed Video Quality through a Neural Netw...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Panneerselvam, Karthick Rajalakshmi, K. Sornalatha, P. Kpr Institute of Engineering and Technology Department of Computer Science & Engineering Tamil Nadu Uthupalayam641407 India Cmr Institute of Technology Department of Mca Karnataka Bengaluru560037 India Alagappa University Department of Computer Applications Tamil Nadu Karaikudi630003 India

ISBN: (纸本)9798331523923

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. The main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other neural networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. The researchers are also proposing a new data pre-processing method based on the Deep Recurrent neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video Coding (AVC). The results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC. © 2025 IEEE.

关键词： image coding

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19th International conference on Artificial Intelligence: Methodology, Systems, and applications, AIMSA 2024

19th International Conference on Artificial Intelligence: Me...

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19th International conference on Artificial Intelligence: Methodology, Systems, and applications, AIMSA 2024

ISBN: (纸本)9783031815416

The proceedings contain 18 papers. The special focus in this conference is on Artificial Intelligence: Methodology, Systems, and applications. The topics include: Cross-Lingual Style Transfer TTS for High-Quality machine Dubbing;an Approach to Discovering, Tracking Over Time, and Summarizing Publicly Available Information on a Given Topic;reinforcement learning Control of Cart Pole System with Spike Timing neural Network Actor-Critic Architecture;Predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture;deep learning for Multi-class Diagnosis of Thyroid Disorders Using Selective Features;Advanced CNN-SVM machine learning Techniques for Facial Skin Ultrasound image Analysis;Testing the NEAT Algorithm on a PSPACE-Complete Problem;investigating the Regularization of Deep neural networks for Affect Recognition with Relevance-Guided Local Explanations;Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation;Combining Graph NN and LLM for Improved Text-Based Emotion Recognition;a Novel Study on Modelling and Adaptive Optimal Control of a Tubular Reactor Based on Gaussian Processes;converging Dimensions: Information Extraction and Summarization Through Multisource, Multimodal, and Multilingual Fusion;enhancing Question Answering in Lecture Videos with a Multimodal Retrieval-Augmented Generation Framework;agent-Based Simulation Leveraging Declarative Modeling for Efficient Resource Allocation in Emergency Scenarios;enhancing Security in Federated learning: Detection of Synchronized Data Poisoning Attacks;Clinical and Acquisition Data for Optimizing MGMT Methylation Status Prediction: A Comprehensive Ensemble Strategy Emphasizing Non-invasive Approaches.

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Unveiling Cetacean Voices: Entropy-Powered Spectrogram Denoising for Deep learning applications 23rd

Unveiling Cetacean Voices: Entropy-Powered Spectrogram Denoi...

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23rd EPIA conference on Artificial Intelligence, EPIA 2024

作者： Bicudo, Francisco Cavaco, Sofia Freitas, Luís Verborgh, Philippe Esteban, Ruth Gauffier, Pauline Silva, Joaquim Nova School of Science and Technology Costa da Caparica Portugal Museu da Baleia da Madeira Caniçal Portugal

ISBN: (纸本)9783031734991

Acoustic monitoring of cetaceans is crucial for studying and conserving these animals and their environment. With the rising interest in deciphering dolphin and whale communication, and the promise shown by machine learning solutions in this field, the demand for gathering and processing large vocalization datasets is only increasing. In this paper, we propose an entropy-based spectrogram filtering method that removes noise naturally present in recordings of cetacean vocalizations. This method enhances the visual clarity of cetacean spectrograms, aiding biologists, and improves vocalization detection when using image-based convolutional neural networks. Its implementation focuses on efficiency, processing 256 times faster than similar filters. The resulting spectrograms achieve on average a 98.73% reduction in required storage space and allow for a segmentation technique that reduces labelling and classification times in machine learning solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spectrographs

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Enhanced Surveillance Through YOLOv3-Based on Deep learning for Real-Time Weapon Detection 1

Enhanced Surveillance Through YOLOv3-Based on Deep Learning ...

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1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Shalini, A. Satya Charan, V. Khan, Khyrul Mateen Nithin, K. Shantagiri, Vaishnavi Srivatsav, B Rohan New Horizon College of Engineering Affiliated to VTU Department of Information Science and Engineering Bengaluru India New Horizon College of Engineering Affiliated to VTU Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350355611

For public safety requirements, the use of machine learning in developing weapon detection in surveillance footage is essential. We apply YOLO v3, which is based on convolutional neural networks (CNN) for image and video analysis that helps in distinguishing weapons from non-weapon objects. We train our models on a large variety of datasets to increase their accuracy, and we utilize transfer learning strategies to lower the number of false positives and negatives. According to the early findings, machine learning holds great promise for improving real-time weapon detection. This work presents a low- cost system design in case study form, demonstrating applicability to large security projects for diverse environments like airports or public events where different types of threats have to be addressed with limited resource allocation but still effective enough towards ensuring safety and security within those areas. © 2025 IEEE.

关键词： Airport security

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

引用

7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer Science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the IEEE conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Medical image Segmentation Techniques: Advances and Challenges

Medical Image Segmentation Techniques: Advances and Challeng...

引用

International image processing, applications and Systems conference (IPAS)

作者： Hela Boulehmi Rania Linda Filali ISET de Bizerte DGET Bizerta Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging.

关键词： Deep learning image segmentation Thresholding (Imaging) machine learning algorithms image edge detection Focusing Feature extraction Convolutional neural networks Medical diagnostic imaging Active contours

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The Analysis of Satellite images for Anomaly Identification in Patterns

The Analysis of Satellite Images for Anomaly Identification ...

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2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Tiwari, Mayur V. Dudul, Sanjay V. Rokade, Ashay I. Jawanjal, Ranjana D. Bhopale, Ashish S. Mahore, Amol S. SGBAU Department of Applied Electronics Amravati India PRMIT&R Department of Electronics & Telecommunication Engineering Badnera India

ISBN: (纸本)9798331512965

Anomaly detection in remotely sensed imagery constitutes a pivotal endeavor with a multitude of applications, encompassing areas such as environmental surveillance and precision agriculture. A variety of methodologies have been formulated to augment the identification of anomalies, defined as deviations from anticipated patterns within image datasets. These approaches capitalize on sophisticated computational strategies and advanced machine learning algorithms to enhance both accuracy and operational efficiency. Anomaly detection in remotely sensed imagery can be implemented through an array of techniques, including heterogeneous and Edge Computing (EC), CNN, feature spaces with multi-dimensionality, unified anomaly detection frameworks, and unsupervised learning for the identification of burnt areas, among others. This manuscript elucidates various methodologies and state-of-the-art technologies pertinent to anomaly detection. Although each of these methodologies exhibits significant advancements, obstacles and constraints continue to exist pertaining to the requirements for digital assets and the imperative for real-time processing functionalities. Subsequent study endeavors may prioritize the refinement of these models to enable more extensive applications and to improve their flexibility in accommodating emerging data sources. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient neural Architecture Search: Computational Cost Reduction Mechanisms in DeepGA 17th

Efficient Neural Architecture Search: Computational Cost Red...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International conference on Artificial Intelligence, MICAI 2024

作者： Barradas-Palmeros, Jesús-Arnulfo López-Herrera, Carlos-Alberto Acosta-Mesa, Héctor-Gabriel Mezura-Montes, Efrén Artificial Intelligence Research Institute University of Veracruz Xalapa Mexico

ISBN: (纸本)9783031838811

neural Architecture Search (NAS) aims to automate the design process of Deep neural networks (DNN) without requiring profound domain knowledge. The Deep Genetic Algorithm (DeepGA) was proposed to find the architectures of Convolutional neural networks (CNNs) for image processing, and its applications have covered a variety of data domains. Nonetheless, one of the main impediments of NAS is its computational cost, which is produced by evaluating candidate architectures. This work proposes using two cost-reduction mechanisms applied to DeepGA: using memory to avoid repeated evaluations and reducing the number of epochs for training in a low-fidelity estimation scheme for evaluations. The results indicate that the execution time of the algorithm was extensively reduced without diminishing the accuracy performance of the resulting architecture. The previous derives in a more efficient NAS procedure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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