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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Prediction of Colon Cancer using DenseNet121, CNN, and REsNET50 Machine Learning Models and using image processing Techniques

Prediction of Colon Cancer using DenseNet121, CNN, and REsNE...

引用

2023 International conference on Artificial Intelligence Robotics, Signal and image processing, AIRoSIP 2023

作者： Hasan, Mahadi Islam, Jahirul Ahmed, Minhaz Hasan, Md Maruf The University of Tennessee at Chattanooga Ms in Data Analytics ChattanoogaTN United States New Mexico Institute of Mining and Technology Ms in Cse SocorroNM United States Ms in Cse *** United States The University of South Dakota Department of Comuter Science VermillionSD United States

ISBN: (纸本)9798350327472

Colon cancer is a matter of great importance in the field of global health, as it stands as one of the primary contributors to mortality rates associated with cancer. The timely identification and precise prognostication of colon cancer have the potential to significantly enhance patient outcomes and diminish mortality rates. The global public health systems face a significant challenge in addressing the impact of colon cancer, which underscores the need for efficient screening and prediction techniques. Machine learning techniques present potential solutions by utilizing extensive datasets to detect intricate patterns that may be imperceptible to human observers. This study investigates the viability of employing machine learning techniques to predict colon cancer by utilizing a dataset consisting of 10,000 colonoscopy images that have been classified into two categories: cancerous and non-cancerous cases. The main aim of this study is to assess the efficacy of various machine learning algorithms, namely DenseNet-121, CNN, and ResNet50, in the prediction of colon cancer. The models' performance was comprehensively assessed in terms of their accuracy in predicting colon cancer. Among the models that were evaluated, DenseNet-121 demonstrated exceptional performance, achieving the highest accuracy of 99.6%. © 2023 IEEE.

关键词： Lung cancer

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Determining image Deblur, Detecting Faces, and Enhancing Appearance Using image processing Techniques

Determining Image Deblur, Detecting Faces, and Enhancing App...

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IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Norazliani Md Sapari Khairul Huda Yusof Nicholas Tan Kien Lung Madihah Md Rasid Siti Maherah Hussin Centre of Electrical Energy Systems (CEES) Faculty of Electrical Engineering UTM Skudai Johor Faculty of Information Science & Engineering Management & Science University Selangor Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

This paper explores the utilization of MATLAB for digital signal processing (DSP) techniques in image processing tasks, focusing on image deblurring, face detection, and facial feature enhancement. Blind deconvolution methods are employed to address image blurriness, while face detection is facilitated using cascaded object detectors. Enhancements to detected facial features involve histogram equalization, smoothing filters, skin tone adjustment, and contrast enhancement techniques, followed by seamless integration using resizing methods. MATLAB serves as a robust platform for implementing and analyzing DSP algorithms, providing insights into practical solutions for common challenges in digital image processing.

关键词： Visualization Histograms Digital images Signal processing algorithms Digital signal processing Skin image restoration

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RNCE: A New image Segmentation Approach

RNCE: A New Image Segmentation Approach

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International conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Kumar, Vikash Ali, Asfak Chaudhuri, Sheli Sinha Electronics and Telecommunication Engineering West Bengal Jadavpur Kolkata700032 India

ISBN: (纸本)9789811978661

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://***/vekash2021/***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection 4

Enhanced YOLOv8 for rapid diagnosis and analysis of infrared...

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4th International conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Zhou, Ming Wu, Qiang Xiong, Wei Ma, Li Cheng, Zhao Sun, Liping State Grid Hubei Electric Power Company Wuhan China Economic and Technological Research Institute State Grid Hubei Electric Power Company Wuhan China

ISBN: (纸本)9781510686106

Insulator defects in power transmission systems pose significant risks to grid stability and safety. This paper presents an improved method for rapid diagnosis of insulator defects using infrared images, based on an enhanced YOLOv8 algorithm. Our approach incorporates Haar wavelet downsampling and a Polarized Self-Attention (PSA) mechanism to boost detection accuracy while maintaining real-time performance. The Haar wavelet downsampling Boosts the model's capability to grasp multi-scale features, concurrently enhancing the PSA mechanism's emphasis on pertinent thermal patterns. We evaluate our method on a comprehensive dataset of 10,000 infrared images collected from various power transmission environments. Experimental results demonstrate that our improved YOLOv8 model achieves SOTA, with a mean Average Precision of 94.7%, outperforming the original YOLOv8 and other object detection algorithms. The proposed method offers a 3.2% increase in mAP compared to the original YOLOv8, while maintaining a processing speed of 42.8 frames per second. This research contributes significantly to the advancement of more efficient and precise outcomes power grid maintenance techniques, potentially reducing the need for manual inspections and enhancing overall system reliability. © 2024 SPIE.

关键词： image enhancement

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An Efficient Workload Distribution Mechanism for Tightly Coupled Heterogeneous Hardware 6th

An Efficient Workload Distribution Mechanism for Tightly Co...

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6th World conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Rivera-Alvarado, Ernesto Torres-Rojas, Francisco J. Computer Science Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (纸本)9789811976599

The Accelerated processing Units (APUs) are tightly integrated heterogeneous computing platforms that contain a CPU and GPU in a single integrated circuit. As their internal computing resources use the same memory (RAM), they can efficiently share data structures. This opens up the opportunity for creating new types of specialized algorithms for the APU. In this research, we designed a specialized load distribution mechanism for the APU and used image rendering as our test load as it is a highly expensive computational task. We found out that our workload distribution mechanism provides better performance than conventional workload mechanisms for GPUs and multicore CPUs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： APU Computer architecture Heterogeneous computing image rendering SoC System-on-chip Tightly coupled heterogeneous hardware Workload distribution

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Automatic Classification and Recognition of Tourist Attraction images using k-Means Algorithm and image processing Technology

Automatic Classification and Recognition of Tourist Attracti...

引用

Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Qian Jiang School of Public Administration Chongqing Vocational College of Transportation China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study investigates the automatic classification and recognition of tourist attraction images through an enhanced K-means algorithm paired with image processing techniques. Given the growing interest in efficient tourist image management and promotion, our research presents a system that significantly advances the processing and categorization of scenic spot images. We implemented a variety of image preprocessing steps—such as grayscale conversion, normalization, Gaussian filtering, and edge detection — to improve the accuracy of feature extraction. Our method involved an optimized K-means algorithm, modified to select initial centroids more effectively and adjust cluster numbers dynamically. Experimental results, based on a dataset of 150 images, demonstrated a classification accuracy of 92%, with the system maintaining robust performance even as dataset sizes increased, confirming the algorithm's efficiency and stability. The processing time for 5000 images was recorded at 12.3 seconds, showcasing a linear relationship between data size and processing time. These findings highlight the potential of our proposed system in enhancing tourism management and marketing strategies by providing a reliable and scalable image classification solution.

关键词： Accuracy Filtering image processing image edge detection Heuristic algorithms Clustering algorithms Feature extraction Classification algorithms image preprocessing Reliability

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks 19

Comparison of algorithms for contrast enhancement based on T...

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conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Wegner, Daniel Kessler, Stefan Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510655461

A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for constrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the constrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

关键词： CNN TOD image contrast enhancement advanced digital signal processing imager assessment method comparison

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Synthetic image Generation With a Fine-Tuned Latent Diffusion Model for Organ on Chip Cell image Classification 26

Synthetic Image Generation With a Fine-Tuned Latent Diffusio...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Ivanovs, Maksims Leja, Laura Zviedris, Karlis Rimsa, Roberts Narbute, Karina Movcana, Valerija Rumnieks, Felikss Strods, Arnis Gillois, Kevin Mozolevskis, Gatis Abols, Arturs Kadikis, Roberts Institute of Electronics and Computer Science Riga Latvia Cellbox Labs Riga Latvia Latvian Biomedical Research and Study Centre Riga Latvia

ISBN: (纸本)9798350304985

Augmentation of the datasets of authentic microscopy images with synthetic images is a promising solution to the problem of the limited availability of biomedical data for training deep neural network (DNN) based classifiers. In the present study, we use a text-to-image latent stable diffusion model fine-tuned by means of low-rank adaptation (LoRA) to augment a small dataset of the images of organ on chip cells. While the resulting synthetic images appear quite similar to the authentic images on which the low-rank adaptation was performed, we find that neither training the EfficientNetB7 DNN model solely on the synthetic data nor augmentation of the real-world dataset with different proportions (10, 25, 50, and 75 percent) of these data leads to the improvement of the accuracy of the model. The findings of our study suggest that a further exploration of the low-rank adaptation options is needed to fully use the capacity of latent diffusion models for the synthesis of biomedical images. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Neural network models

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image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International conference on Computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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