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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"

共 6052 条记录，以下是21-30 订阅

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processing and Classification Algorithms of Medical images 6

Processing and Classification Algorithms of Medical Images

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Seitaj, Oltiana Mechanics University of Roma Tre Department of Industrial Engineering Electronics Rome Italy

ISBN: (纸本)9798331506520

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional neural networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep Residual learning for Fruits of Ceremonial Plants Recognition 2

Deep Residual Learning for Fruits of Ceremonial Plants Recog...

引用

2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Negara, Ida Bagus Kade Dwi Suta Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Udayana University Denpasar Indonesia

ISBN: (纸本)9781643685762

Fruit detection refers to a method within image processing and computer vision that focuses on automatically recognizing and distinguishing different types of fruits using advanced algorithms and techniques. Fruits are essential and widely used in ceremonial offerings (Banten). They are pivotal in ensuring the completeness of ceremonial offerings. The main goal of fruit detection is to recognize fruits in image form, making it applicable in various applications, including inventory management, automatic classification in the agricultural industry, and medical applications for monitoring dietary patterns. Commonly used methods include artificial neural networks (deep learning), digital image processing, and feature extraction techniques to distinguish between shapes, colours, textures, and other visual features of different types of fruits. In this research, the deep learning algorithm CNN (Convolutional neural Network) is employed. This technique, a deep learning algorithm, excels in accurate image recognition and classification. A total of 500 fruit images are used for five types of ceremonial fruits commonly used in religious activities, namely coconut, banana, candlenut, nutmeg, and areca nut. The residual learning method RESNET152 is used as the CNN algorithm architecture. According to the test findings, the highest accuracy rate achieved was 93% in correctly identifying ceremonial fruit images. © 2025 The Authors.

关键词： Convolutional neural networks

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Advanced Detection of Copy-Move Forgery Using Deep Convolutional neural Network Features Integrated with machine learning-Based Classification Techniques 3rd

Advanced Detection of Copy-Move Forgery Using Deep Convoluti...

引用

3rd International conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Al-khuzaie, Mohammed Y. Chohan, Jasgurpreet Singh Sabah, Hawraa Ali Dey, Protyay Verma, Deeksha Hussein, Laith College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq School of Mechanical Engineering Rayat Bahra University Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri- Punjab Mohali140307 India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forgery detection techniques has become paramount to distinguish between authentic and manipulated images. image forensics is utilized in various fields, including national intelligence agencies, scientific publications, and social networks. One specific type of image manipulation, known as copy-move forgery, involves copying and pasting a portion of an image within the same image, resulting in an altered version of the original. In this study, we introduce a lightweight convolutional neural network (CNN) architecture combined with Support Vector machine (SVM) classification for copy-move forgery detection (CMFD). This approach uses a similarity measure across feature patches to identify regions containing forgeries. We evaluated the effectiveness of our model using the challenging MICCF600 dataset commonly employed in CMFD research. The proposed model effectively reduces false positives, thereby enhancing pixel-level accuracy and reliability in CMFD applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Survey on Adversarial Text Attacks on Deep learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

引用

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

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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HPDNET: Hyper Prior Dependent Demosaic neural Network 6

HPDNET: Hyper Prior Dependent Demosaic Neural Network

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Seo, Youngil Jun, Sungho Lim, Dongpan Song, Seongwook Samsung Electronics System LSI Division Yongin Korea Republic of

ISBN: (纸本)9798331506520

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation. © 2025 IEEE.

关键词： image quality

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Perspective Analysis Toward Deep learning Models for Wireless Capsule Endoscopy images for Gastrointestinal Tract 4th

Perspective Analysis Toward Deep Learning Models for Wireles...

引用

4th International conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Sharma, Rakesh Lamba, C.S. Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

Analysis of medical image is new technique to solve the medical problems by examine the images that has been generated through some detailed clinical practice. For better clinical diagnosis, the goal is to extract information in a way that is both affective and efficient. Abdominal bleeding, ulcers, tumors, celiac disease, ulcerative colitis, and other gastrointestinal disorders are difficult to diagnose due to the uncertainty of accessing a volute condition in the human body. WCE is a non-invasive, painless, and patient-controlled gastrointestinal tract examination. Although using deep learning models to detect problems in WCE photos enhances detection accuracy, model training necessitates a large volume of labeled data. These deep models, on the other hand, are difficult to describe and do not incorporate expert input into the model decision-making process. Deep learning-based machine learning breakthroughs have improved the ability to categories, detect, and quantify patterns in medical images. Deep learning approaches are quickly being applied in medical imaging to increase performance in a variety of medical applications. Due to recent advancements in the field of biomedical engineering, medical image analysis has become one of the most important research and development areas. The use of machine learning algorithms for the processing of medical images is one of the reasons for this progress. Deep learning has been successfully applied as a machine learning technology, allowing a neural network to learn information automatically. This contrasts with methods that rely on traditional handcrafted elements. The selection and computation of these characteristics are a difficult task. Deep convolution networks, a type of deep learning approach, are widely employed in medical image processing. With these considerations in mind, the purpose of this research is to see if employing semi-supervised deep learning models over supervised deep learning methods for detecting a

关键词： Endoscopy

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A Multi Model Deep learning Framework for Sarcasm Detection in Regional Social Media: An Overview 1

A Multi Model Deep Learning Framework for Sarcasm Detection ...

引用

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

作者： Bhadgale, Anilkumar M. Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), and Transformers. These architectures, combined with Natural Language processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Convolutional neural networks

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Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs 34th

Elevating Healthcare AI: Achieving Efficiency and Accuracy i...

引用

34th Brazilian conference on Intelligent Systems

作者： Ferreira, Gabriel Bicalho Silva, Pedro Silva, Rodrigo Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil

ISBN: (纸本)9783031790379;9783031790386

machine learning, particularly convolutional neural networks (CNNs), has gained prominence in healthcare applications, including medical diagnosis and clinical support. However, the increasing size of CNNs poses challenges in resource-constrained medical devices and real-time applications. This paper explores the effectiveness of pruning and quantization on the ResNet50 model within the MedMNIST dataset, a valuable resource for medical image classification. The study evaluates a surrogate-based multiobjective compression method on three MedMNIST datasets: RetinaMNIST for diabetic retinopathy grading, DermaMNIST for disease categorization, and BloodMNIST for blood cell classification. Results demonstrate that the proposed compression method successfully identifies less computationally intensive models while maintaining or improving accuracy across all three healthcare-related datasets. A reduction of about 50% in inference time and an increase of more than 1% in accuracy were observed. These findings emphasize the practicality of compression techniques in healthcare applications, particularly for resource-constrained environments and real-time decision-making scenarios. This research opens avenues for further validation and exploration in other healthcare-related applications with higher-quality neural network models, ultimately enhancing the deployment of machine learning in the healthcare domain.

关键词： Healthcare Artificial intelligence Convolutional neural networks neural Network Compression

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive learning

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Retinal image Analysis for Detection of Diabetic Macular Edema Using Transfer learning 8

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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8th International conference on Data Science and machine learning applications, CDMA 2025

作者： Asim, Tayyaba Akbar, Shahzad Shahzore, Usama Rehman, Amjad Saba, Tanzila Ayesha, Noor Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan450001 China

ISBN: (纸本)9798331539696

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. Therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. The proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets;the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional neural networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional neural networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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