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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31-40 订阅

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Exploring Advanced Deep learning Techniques for Multi-Class Melanoma Cancer Classification 6

Exploring Advanced Deep Learning Techniques for Multi-Class ...

引用

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

作者： Alhasson, Haifa F. Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia

ISBN: (纸本)9798331506520

Melanoma represents one of the most lethal forms of skin cancer, underscoring the importance of early detection for effective treatment and improved survival rates. Traditional diagnostic methods, which predominantly rely on visual inspection and biopsies, are often time-consuming and susceptible to human error. Timely diagnosis significantly enhances the likelihood of recovery and can reduce healthcare costs by minimizing the necessity for surgical, radiographic, or chemical treatments. Recently, deep learning techniques have demonstrated considerable promise in automating and improving the accuracy of medical diagnoses, including melanoma classification. In this study, we evaluate the performance of several state-of-the-art deep learning models - DenseNet, ResNet, VGG-16, VGG-19, Inception v3, and AlexNet - for multi-class melanoma cancer classification. Our objective is to identify the model that offers the best performance in terms of accuracy, sensitivity, and specificity. We conduct a comprehensive comparison using publicly available datasets, such as HAM10000 ("Human Against machine with 10000 training images"), to ensure robust and generalizable results. This evaluation aims to advance the field of melanoma diagnosis by identifying the most effective deep learning approach, thereby facilitating early and accurate detection of this life-threatening disease. © 2025 IEEE.

关键词： Convolutional neural networks

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Improved image Classification with Manifold neural networks

Improved Image Classification with Manifold Neural Networks

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deberaldini Netto, Caio F. Wang, Zhiyang Ruiz, Luana Johns Hopkins University Baltimore United States Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States

ISBN: (纸本)9798350368741

Graph neural networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs' message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs' potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks. © 2025 IEEE.

关键词： generalization graph neural networks manifold neural networks variational autoencoders

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Text Recognition in Persian Documents Using Self-Supervised learning 6

Text Recognition in Persian Documents Using Self-Supervised ...

引用

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

作者： Babaei, Atefeh Dehghani, Alireza Yazdi, Mehran Jamshidi, Ali Rasouli, Mahdi Shiraz University School of Electrical and Computer Engineering Shiraz Iran CeADAR Ireland's Centre For AI Dublin Ireland Université Grenoble Alpes School of Computer Science and Mathematics Grenoble France

ISBN: (纸本)9798331506520

Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like Persian, presents unique challenges due to issues such as font types, word structures, and the scarcity of labeled data. This paper addresses these challenges by implementing deep learning techniques, specifically self-supervised learning with convolutional and recurrent neural networks (CNNs and RNNs). Our approach successfully mitigates the data scarcity problem, achieving an accuracy of 92.9% on the prepared dataset. © 2025 IEEE.

关键词： Self-supervised learning

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

引用

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

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional neural networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Investigating the Use of Generative Adversarial networks and Recurrent neural networks for Medical image Segmentation 5th

Investigating the Use of Generative Adversarial Networks and...

引用

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

作者： Kumar, Deepak Wadhwa, Bhawna Krishnamoorthy, Ramkumar Agarwal, Ankita Department of Mathematics Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the usage of generative opposed networks (GANs) and recurrent neural networks (RNNs) for medical photo segmentation. First, clinical picture segmentation is described and discussed, after which the two fundamental methods of segmentation, particularly supervised and unsupervised studying, are reviewed. Most generative models are reviewed, including generative opposed networks (GANs) and recursive neural networks (RNNs). Primarily, the paper specializes in utilizing GANs and RNNs for scientific image segmentation. The contribution of this paper is threefold. First, it provides an in-depth review of the nation of the artwork in generative fashions, focusing on the software of GANs and RNNs for scientific photograph segmentation. 2nd, it provides a discussion of the challenges and possibilities of the usage of GANs and RNNs for medical image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Precision diagnosis of brain tumors: An overview of advanced machine learning techniques 1st

Precision diagnosis of brain tumors: An overview of advanced...

引用

1st International conference on applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Shivani Agrawal, Krishna Kant Agrawal, Gaurav School of Computer Science and Engineering Galgotias University Uttar Pradesh India

ISBN: (纸本)9781032874036

This paper provides a comprehensive examination of deep learning techniques for brain tumor segmentation in magnetic resonance imaging (MRI) data, delving into the various methods utilized. It begins with an overview of traditional segmentation techniques, emphasizing the limitations in accurately capturing tumor boundaries and appearances. Deep learning, particularly convolutional neural networks (CNNs), is introduced as an effective alternative due to its ability to handle complex image features. The review offers an in-depth analysis of recent advancements in deep learning-based brain tumor segmentation, highlighting various convolutional neural network (CNN) architectures such as U-Net and DeepMedic. It discusses the efficacy, computational efficiency, and robustness of these methods across different tumor types and MRI modalities. The paper addresses key challenges and future directions in the field, including the need for large, annotated datasets, improving model interpretability, integrating with clinical workflows, and mitigating class imbalance and data variability. The paper concludes by highlighting the transformative potential of deep learning in enhancing brain tumor segmentation, leading to more accurate diagnosis and treatment planning. Researchers aim to advance understanding of tumor biology, predict treatment outcomes, and ultimately improve patient care in neuro-oncology by harnessing the capabilities of deep learning. © 2025 the Author(s).

关键词： Convolutional neural networks

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Advanced Deep learning Strategies for Breast Cancer image Analysis 6

Advanced Deep Learning Strategies for Breast Cancer Image An...

引用

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

作者： Slimi, Houmem Cherif, Imen Abid, Sabeur Sayadi, Mounir University Of Tunis Dept. Electrical Engineering Tunis Tunisia University Of Tunis Dept. Electronics Tunis Tunisia

ISBN: (纸本)9798331506520

Breast cancer (BC) is a leading cause of cancer-related deaths in women, but early detection significantly improves survival rates. Recently, deep learning neural networks have shown potential for enhancing BC screening, identification, and classification. In this research, we propose a modified deep learning model incorporating customized architecture adjustments and advanced data augmentation techniques for early BC detection and classification from mammography images. Our model achieves a 40% increase in accuracy compared to state-of-the-art transfer learning methods on the MIAS and SA datasets, demonstrating improved robustness and precision. However, while our results are promising, the limited size and diversity of the datasets suggest that further studies are needed to validate the generalizability of the model across broader, more varied data sources. © 2025 IEEE.

关键词： Lung cancer

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Enhanced image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. image captioning combines image recognition and Natural Language processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

引用

3rd International conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Advanced CNN-SVM machine learning Techniques for Facial Skin Ultrasound image Analysis 19th

Advanced CNN-SVM Machine Learning Techniques for Facial Skin...

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

作者： Nabeel, Aayad Ragheb, Mostafa Momcheva, Galina Kamar, Issa Hamady, Mohamad Al-Mansour University College Baghdad Iraq Faculty of Sciences Lebanese University Tripoli Lebanon Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria Arts Sciences and Technology University in Lebanon Beirut Lebanon

ISBN: (纸本)9783031815416

This study investigates the machine learning techniques for unsupervised image classification and quality assessment in the domain of ultrasound imaging. Leveraging Convolutional neural networks (CNNs) for feature extraction and subsequent integration into a Support Vector machine (SVM) model, we explored a novel approach aimed at accurate image classification. The dataset comprises high-frequency images in the form of image sequences depicting the facial skin of females. The study's primary emphasis was to categorize ultrasound images based on learned deep features, offering a distinctive framework for unsupervised image classification. The investigation employed CNNs to extract deep features from images, enhancing the SVM model's performance in accurately categorizing images. The incorporation of gamma correction as a preprocessing step further augmented the accuracy and sensitivity of the models. The SVM model exhibited exceptional performance, achieving accuracy rates exceeding 95.43% in the training phase and approximately 94.72% during testing, that is a significant milestone in the precise classification of ultrasound images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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