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检索条件"任意字段=4th International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2024"

共 127 条记录，以下是31-40 订阅

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Hematological image Analysis: Enhancing Blood Cell Segmentation through advanced image processing Techniques 4

Hematological Image Analysis: Enhancing Blood Cell Segmentat...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Sandhiya, B. Banu, Shahira N. Annapoorna, Vidy C. Easwari Engn Coll Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

this research study intends to solve the severe problem of poikilocytosis, a disorder that causes red blood cells (RBCs) to take on aberrant forms. this system analyzes blood smear images captured with a compound microscope by using sophisticated image processing algorithms. the method's main component is the application of the Faster R-CNN algorithm. It not only recognizes different RBC forms but also classifies them, providing information about possible related illnesses. the method significantly increases the dataset over existing methods while utilizing Faster R-CNN's better efficiency. the erythrocyte and BCCD databases data combines to create this expansion, which includes a wide range of RBC forms. the speed and precision of RBC shape detection is improved for poikilocytosis diagnosis by utilising Faster R-CNN. the larger dataset provides a deeper investigation of the wide variety of RBC forms, increasing the likelihood of finding connections to different blood cell-related illnesses. the main objective of this research study is to present a thorough report that improves patient outcomes, healthcare practices by advancing the diagnosis and knowledge of blood cell-related illnesses. this initiative paves way for better patient care and diagnostic accuracy by supporting continuing efforts to improve healthcare and medical diagnostics.

关键词： Blood cell Poikilocytosis Faster-rcnn image processing

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advanced Deep Learning Methods for Early Detection and Progressive Classification of Cervical Cancer 4

Advanced Deep Learning Methods for Early Detection and Progr...

引用

4th international conference on Artificial Intelligence and signal processing

作者： Handa, Keshav Sharma, Viabhav Meenakshi, N. SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur Tamil Nadu India

ISBN: (纸本)9798350350661;9798350350654

the proliferation of deep learning methodologies, especially convolutional neural networks (CNNs), has significantly improved the early detection and classification processes for cervical cancer, a predominant health concern for women globally. this research puts forth a sophisticated CNN model optimized to harness a comprehensive dataset enabling enhanced feature extraction from medical images, thereby elevating diagnostic precision. Incorporating transfer learning from adeptly trained models, this strategy not only augments the efficiency of our network but also accelerates the training process, thus ensuring a swifter adoption in clinical practice. the performance of the proposed model is meticulously assessed using accuracy and loss as the principal metrics. these metrics demonstrate a marked improvement in the model's ability to discern patterns indicative of cervical cancer, hence offering a superior alternative to conventional diagnostic methods. the encouraging outcomes of this study underscore the viability of deploying our CNN model as an integral component in clinical diagnostics, charting the course towards expedited and more precise patient evaluations. this investigation exemplifies the transformative impact of artificial intelligence on healthcare, signifying considerable progress in the prompt and accurate diagnosis of cervical cancer.

关键词： Cervical cancer convolutional neural networks deep learning medical image analysis transfer learning diagnostic precision clinical diagnostics artificial intelligence

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2024 IEEE 4th international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2024

2024 IEEE 4th International Conference on Applied Electromag...

引用

4th IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2024

ISBN: (纸本)9798331528171

the proceedings contain 84 papers. the topics discussed include: design of a reconfigurable dual-band slot antenna with mems switches;enhancing spectrum busting: a federated learning approach for radio fingerprinting for defense forces;a correlational study of wireless fading channels: performance analysis over symbol error probability;enhancing security in 5g vehicular networks through machine learning-based attack detection;implementation of a deep learning technique for detection of objects on roads for autonomous vehicles;harmonics phasor estimation by using sparse based sigmoid least mean mixed norm (SLMMN) approach;analysis and design of Koch fractal antenna using metamaterial with improved performance;leveraging advanced deep learning techniques for automated dermatological image classification;a fuzzified Euclidean framework for accurate cervical cancer cell segmentation and nuclei detection;and compact dual-port circularly polarized wideband MIMO antenna with an effective boresight polarization characteristics.

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advanced Pansharpening Techniques for Satellite image Fusion 4

Advanced Pansharpening Techniques for Satellite Image Fusion

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Kamala, B. Ramana, V. Giridharan, S. Ranjani, J. Sri Sairam Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

this research develops an algorithm for efficiently fusing multiple satellite images, addressing challenges like varying spatial resolutions, spectral bands, radiometric differences, and geometric distortions. Objectives include creating a robust preprocessing pipeline, evaluating fusion algorithms, using quality metrics to assess performance, exploring applications such that land cover classifier and change detection and benchmarking against existing methods. the aim is to enhance satellite image processing, aiding environmental monitoring and urban planning. the proliferation of satellite sensors, each with unique strengths and weaknesses, has increased the need for image fusion to derive comprehensive information from datasets. Fusion techniques leverage the strengths of individual sensors, providing detailed spatial information, spectral diversity, and temporal variations. this integration leads to improved image quality, enhanced feature extraction, and more accurate analysis. the demand for satellite imagery in agriculture, environmental monitoring, urban planning, and disaster management drives the development of advanced algorithms for producing more informative and actionable data. © 2024 IEEE.

关键词： Satellite imagery

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CScGAN: Leveraging Compressed Sensing and Generative Adversarial Networks for image Super-Resolution Reconstruction 4

CScGAN: Leveraging Compressed Sensing and Generative Adversa...

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4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Li, Chang Wang, Xin Li, Zhengyu School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350368239

We propose a novel image super-resolution technique, the Compressive Sensing combined with Generative Adversarial Network model (CScGAN). During the image super-resolution reconstruction process, artifacts that arise from the complexity of the reconstruction network, imbalances in training data, and instability in the training process can degrade the quality of the reconstructed images. Our method concentrates on minimizing reconstruction artifacts through advanced signal processing and structural optimization of the network. We elucidate our CScGAN model using architectural diagrams and mathematical notation. When compared with multiple public datasets and state-of-the-art methods, our approach significantly reduces the impact of artifacts during the reconstruction process and enhances the quality of the reconstructed images. ©2024 IEEE.

关键词： compressed sensing generative adversarial network image reconstruction layered image representation

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the application of machine learning algorithms in image processing denoising 4

The application of machine learning algorithms in image proc...

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4th international conference on advanced algorithms and Neural Networks, AANN 2024

作者： Lu, Di Xiong, Zhengqiang Wuhan Business University WuHan HuBei430000 China

ISBN: (纸本)9781510686106

In order to solve the problem of losing the original information of images in traditional image processing methods during denoising, this paper proposes an application method of machine learning algorithm in image processing denoising. this also means that it is necessary to continuously compare the processed image with the original image, which will increase the time required for denoising. However, deep learning techniques have played an important role in this regard. through intelligent technology, especially supervised machine learning algorithms, information images can be better processed, enabling people to observe things more clearly and take correct actions. Specifically, we can use supervised machine learning algorithms to extract noise during image denoising. this method is based on setting separation nodes for image noise at multiple points, and then using clustering theory to filter out noise, thereby completing image denoising. the experimental results show that using animal images as the test object, we can detect the noise contained in them, and by comparing the effects of the original method and the new method, we found that the new method can obtain results consistent with the original image. More importantly, without introducing image distortion, the new method controls the denoising time to 1.15 seconds, while the original method takes an average of 20.21 to 15.13 seconds. this indicates that the new method can significantly improve the efficiency of denoising and has the potential for practical applications. © 2024 SPIE.

关键词： Contrastive Learning

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A MixIST image restoration algorithm based on IST

A MixIST image restoration algorithm based on IST

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4th international conference on the Applications of Digital Information and Web Technologies, ICADIWT 2011

作者： Huang, Minghu Cao, Hanqiang Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424498246

Iterative Shrinkage thresholding (IST) algorithm has been becoming popular in image restoration as well as signal processing communities. Analysis and experiments expose the low efficiency and poor robustness of IST. In the paper, we devised a Mixed Iterative Shrinkage thresholding (MixIST) algorithm which bears more robustness, higher efficiency and better efficacy. By the power of two step iteration, advanced pre-filtering, better shrinkage methods, unbiased thresholding and the increased detail information of the original image by redundant wavelets, MixIST achieve a further improvement of the quality of restored image. In order to show the actual performance of MixIST, results from experiments on image restoration as well as comparisons with other algorithms are demonstrated. © 2011 IEEE.

关键词： Wavelet transforms

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image dehazing algorithm of transmittance fusion based on light field depth information 4

Image dehazing algorithm of transmittance fusion based on li...

引用

4th international conference on signal image processing and Communication, ICSIPC 2024

作者： Ma, Jinshan Wang, Changxiang School of Computer and Information Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9781510682467

An algorithm for dehazing images is presented in this study, which combines fusion transmittance and light field depth information to address issues such as color distortion and loss of image details that commonly occur in traditional dehazing algorithms relying on atmospheric scattering models with imprecise parameter estimation. this algorithm utilizes light field multi-view technology to estimate depth, gather depth information of foggy scenes, outline the sky region within the scene, and segment the image into foreground and sky regions. Transmittance values are calculated separately for each region, and then a confidence fusion process is applied to determine the final transmittance value. the obscured image is then enhanced using the atmospheric scattering model to achieve dehazing results. Experimental results demonstrate that this algorithm surpasses current single-image dehazing algorithms in various fog density scenarios by producing superior dehazing results, effectively reducing color distortion and halo effects while also recovering more image details. © 2024 SPIE.

关键词： image segmentation

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Clinical Breast Imaging and Deep Learning Model For image Malignancy Prediction Using Resource-Constrained Embedded Devices 4

Clinical Breast Imaging and Deep Learning Model For Image Ma...

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4th international conference on Artificial Intelligence and signal processing

作者： Chokhandre, Sumedha R. Jadhav, Ms Pranjali Gole, Nilesh thakare, Amit N. Bondade, Akansha R. Singh, Priyanka YCCE Dept Informat Technol Nagpur Maharashtra India Vishwakarma Inst Informat Technol AI & DS Dept Pune Maharashtra India Vidharbha Inst Technol Dept CSE Nagpur Maharashtra India Cummins Coll Engn Women Nagpur Maharashtra India G H Raisoni Univ Dept CSE Saikheda India VIT AP Univ Sch Comp Sci Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. the findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Computer assisted diagnosis(CAD) Convolutional neural network (CNN) Artificial Intelligence (AI) cloud based data analytics

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Intelligent Bone Fracture Recognition Framework: Convolutional Neural Network(CNN): Note: Sub-titles are not captured in Xplore and should not be used 4

Intelligent Bone Fracture Recognition Framework: Convolution...

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4th IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2024

作者： Mishra, Debani Prasad Bhal, Arpita Kumar, Ayush Pradhan, Anirudh International Institute of Information Technology Bhubaneswar India International Institute of Information Technology Bhubaneswar India

ISBN: (纸本)9798331528171

In this study, we introduce a method to diagnose bone disease using advances in machine learning (ML) and deep learning (DL) technologies. Our scheme combines several different technologies with convolutional neural networks (CNN) to distinguish healthy bones. this new hybrid model represents a breakthrough in bone diagnosis and provides a significant performance in computation time compared to existing deep CNN models. the main innovation is that we use an improved sharp-edge algorithm to accurately identify cracks in X-ray images. then the grayscale image and the corresponding image are input into the algorithm for training and evaluation. We perform a performance test evaluating our method against a state-of-the-art deep CNN model using skeletal muscle data. Our results showed the best performance in fracture diagnosis, achieving irregularity, F1 score, and recall of 96.12%, 99%, and 98%, respectively. this demonstrates the effectiveness of combining algorithms to improve CNN performance, demonstrating the potential of our method to revolutionize bone diagnosis in clinical practice. © 2024 IEEE.

关键词： Convolutional neural networks

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