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Brain tumor image recognition method based on convolutional neural network

Brain tumor image recognition method based on convolutional ...

引用

2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Tie, Ranran Zhang, Shixiang Chao, Zengying Chen, Genxiu Wang, Shuang Liu, Bocheng Second Clinical Medical School of Nanchang University Nanchang 1299 Xuefu Road Jiangxi Nanchang China School of Software of Nanchang University 235 Nanjing East Road Jiangxi Nanchang China School of Public Health of Nanchang University 1299 Xuefu Road Jiangxi Nanchang China Karamay Campus 355 Anding road Xinjiang Uygur Autonomous Region Karamay China School of Humanities Jiangxi University of Finance and Economics 169 Shuanggang East Street Jiangxi Nanchang China

ISBN: (纸本)9781510680425

This study focuses on brain tumors, a highly fatal malignant lesion of the cranial cavity, and proposes a brain tumor detection and automatic classification framework based on the VGG16 pre-trained *** employing transfer learning, we incorporated global average pooling layers and fully connected layers into the model, and froze the last layer to prevent overfitting and *** used the focal loss function to optimize model training, enabling it to learn from difficult samples and down-weight simple *** performance evaluation, we compared the improved VGG-GAP model with the original VGG16, VGG19, and InceptionV3, and the results showed significant improvements in all performance metrics. © 2024 SPIE.

关键词： Convolutional neural networks

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Detection Method of Egg Shell Crack Based on image processing

Detection Method of Egg Shell Crack Based on Image Processin...

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Li, Fei Zhang, Ning College of Mechanical Engineering Xijing University Shaanxi Province Xi'an City China

ISBN: (纸本)9781510666351

As eggs are a very important food source in our daily diet, the inspection of egg quality must be strict. In this paper, a detection method based on machine vision image processing is proposed for the detection of eggshell cracks. Through image pre-processing, the images of the three BGR channels are compared, and then the median filtering is used to derive the image information of the R channel that is more favorable for detection. The image is enhanced to find the ROI, and connected region and to perform image segmentation to get the crack information. Finally, a morphological operation is used to extract the feature value of crack length from the image and conclude that the crack in the eggshell is detected by judging the feature value of the image. The experiments show that the accuracy of the OpenCV image processing-based detection method can reach more than 90%, which indicates that the method of detecting eggshells through image processing has unlimited potential. © 2023 SPIE.

关键词： image enhancement

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Knowledge Enhancement and Optimization Strategies for Remote Sensing image Captioning Using Contrastive Language image Pre-training and Large Language Models 24

Knowledge Enhancement and Optimization Strategies for Remote...

引用

international conference.on Machine Intelligence and Digital Applications (MIDA)

作者： Wang, Xinren Wan, Tengfei Song, Jianning Huang, Jingmeng Jiangsu Special Equipment Safety Supervis & Inspe Wuxi Jiangsu Peoples R China Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9798400718144

In this study, we propose an innovative multimodal learning approach that integrates Contrastive Language image Pre-training and large language models to enhance the recognition efficiency of remote sensing images and their capacity to generate related professional information. This method has effectively achieved integration of image processing and text generation at a technical level, exhibiting significant application advantages in fields such as automated Geographic Information Systems construction, environmental monitoring, disaster assessment, and geographic science education. The research underscores the advancements of the Contrastive Language image Pre-training model in visual-textual understanding and the technical strengths of large language models in handling complex text tasks. By designing an integrated fusion layer, we have efficiently combined visual features with textual information and conducted a comprehensive evaluation of the model's recognition accuracy and text generation quality on the dataset. Experimental results show that our model achieved a recognition accuracy of 73.7% and a text quality score of 26.6, validating its efficacy and powerful capability in dealing with the complexity and diversity of remote sensing images. Through the deep integration of Contrastive Language image Pre-training and large language models, this research not only further advances multimodal learning technologies but also opens new perspectives and possibilities for the research and application of remote sensing image recognition and related information generation.

关键词： Multimodal Learning Remote Sensing image recognition Knowledge Enhancement Large Language Models Caption Generation

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4th international conference.on advances in Computational Science and Engineering, ICACSE 2023

4th International Conference on Advances in Computational Sc...

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4th international conference.on advances in Computational Science and Engineering, ICACSE 2023

ISBN: (纸本)9789819729760

The proceedings contain 52 papers. The special focus in this conference.is on advances in Computational Science and Engineering. The topics include: Sensitivity Analysis on Absorber Column as Hydrogen Gas Purification Unit Using Aspen Plus;use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer;Accelerating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Using Vincenty’s Inverse Method on CUDA;procedural Modeling for Sustainable Urban Development and Planning: A Blender Plugin for 3D Modeling of Philippine Cities;estimating Orangutans Population Size in Sabah Rainforest in Malaysia Using Spatial Modelling;multilayer Gaussian Feature Extraction Algorithm for Sky image Classification;face Expression recognition: A Survey on Hyperparameter Optimization;Enhancing Low Light image Classification Using MADPIP Approach;a Robust Multiple Adaptive Derivative Face recognition System on Pose and Illumination;performance Comparison of Convolutional Neural Network Deep Learning Architectures for Remote Sensing image Segmentation;revolutionizing Human–Computer Interaction: Unraveling the Power of Deep Learning Convolutional Neural Networks in Face recognition;Multiple Adaptive Derivative Passive image processing Approach to Unsharp Masking Restoration for CNN on Low Light images;data-Driven Insights for Strengthening Information Security Awareness in Higher Education Institutions;Determining Motivational Factors for Retention and Course Completion Among Filipino MOOC Learners: A Thematic Analysis;development of Kansei-Based Visualization pattern for E-Learning Website;augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences;classification of Game Mechanics: A Brief Review;challenges and Issues in Team Gamer Loyalty for Massively Multiplayer Online Game;optimizing Cloud-Based Educational Services for Enhanced Learning in Higher Education Institutions;biomarker Identification for Lung Canc

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A novel CNN-Transformer network for cloud detection in remote sensing image

A novel CNN-Transformer network for cloud detection in remot...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Ai, Xinkai Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Shandong Province Qingdao266590 China

ISBN: (纸本)9781510680425

Convolutional neural networks (CNNs) are the mainstream model for extracting rich features in deep learning-driven studies on cloud detection for remote sensing images. However, due to the limitation of receptive fields in convolutional operations, a single CNN has limitations in mining global information and long-range dependencies, which affects the precision of cloud identification in intricate environments. To address the insufficient global feature and long-range dependency modeling of CNNs, a new CNN-Transformer network for cloud detection (CNN-TFCD) in remote sensing images is proposed in this paper. This method comprehensively utilizes strengths of both CNN and transformer to fully mine local spatial details, global contextual information, and long-range dependencies, to obtain comprehensive feature representations. The feature cascade mechanism is applied for multi-scale feature fusion, further enhancing the model's feature representation capability. Evaluations conducted on the Landsat-8 dataset demonstrate that the CNN-TFCD method achieves accurate and stable cloud detection. © 2024 SPIE.

关键词： Convolutional neural networks

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Nonuniformity correction based on the blackbody image space in uncooled long-wave infrared cameras without a shutter

Nonuniformity correction based on the blackbody image space ...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wu, Zenan Zhang, Chengshuo School of Information Science and Engineering Shenyang University of Technology Liaoning Shenyang110870 China

ISBN: (纸本)9781510680425

Uncooled long-wave infrared detectors suffer from temperature drift and image quality degradation due to the lack of a constant temperature environment. A shutter is commonly used to calibrate the detector periodically, but it causes wear and tear on the lens and detector, and may fail in harsh environments. This article presents a more advanced research method for nonuniformity correction based on the blackbody image space in uncooled long-wave infrared cameras without a shutter. The proposed method integrates based on calibration and based on real time scenario non-uniformity correction techniques in two step: offline calibration stage builds an blackbody image space from blackbody images collected at different ambient temperatures;online identification stage finds the optimal projection of the scene image in blackbody image space using the statistical prior of the sharp real time scenario infrared image. And estimates the optimal blackbody image for non-uniformity correction at the current ambient temperature. It creates a "virtual shutter" in front of the lens that effectively compensates for the temperature drift of the detector characteristics and lens. Comparative experiments show that the proposed method significantly reduces non-uniformity and adapts to the ambient temperature variation. © 2024 SPIE.

关键词： Infrared detectors

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A quadrilateral distorted image correction algorithm based on block triangle

A quadrilateral distorted image correction algorithm based o...

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Deng, Xiangyu Zhang, Aijia Ye, Jinhong College of Physics and Electronic Engineering Northwest Normal University GS United States

ISBN: (纸本)9781510666351

In the process of image shooting, due to the shooting angle or the shooting reason, the original image has geometric deformation problems in the geometric position, shape, size and orientation, which brings many inconvenience and difficulties to the following image processing fields such as image fusion, image denoising, image recognition, and image edge detection. In order to further improve the image processing ability and recognition accuracy of distorted graphics, this paper proposes a distortion correction algorithm based on block mapping for quadrilateral target images. This algorithm completes the transformation of the image from irregular to regular by dividing the quadrilateral image into two triangle images and performing homography mapping on the pixel points of the triangle image respectively. A large number of experiments show that the algorithm in this paper is superior to traditional methods such as hough and radon transform, and solves the limitations of traditional mapping algorithm in the process of quadrilateral image correction, improves the correction effect of distorted images, and provides a strong support for the subsequent processing of distorted images. © 2023 SPIE.

关键词： image fusion

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MdcFormer: transformers based on dynamic weights and multiscale for medical image segmentation

MdcFormer: transformers based on dynamic weights and multisc...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Ma, Chenyang Wang, Xiaoru Deng, Bowen Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510680425

Medical images often consist of multiple modalities, such as multimodal MRI images commonly used in diagnosing and studying brain tumors., and multimodal images provide rich complementary information. In the past, multimodal image segmentation usually directly added or connected modal features in the early or middle stage, which made it difficult to obtain the connection between modal features. In addition, there is a difference in information between modals and modals, and the previous method did not dealign modal features, which is likely to lead to reduced the effect of modal fusion. Thus, we propose a Multiscale dual dynamic feature fusion transfromer (MdcFormer) model to explore the effects of multi-scale features, spatial and channel dynamic fusion and modal feature alignment on the segmentation effect of multimodal medical images. Utilizing a multi-encoder configuration and a single decoder, we gather characteristics from various modes at various levels and blend them in a dynamic manner across both spatial and channel domains. The proposed approach was evaluated using the BraTS2020 benchmark dataset. Empirical findings indicate that the model enhances the precision of segmentation. © 2024 SPIE.

关键词： image segmentation

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proceedings - 2024 international conference.on advances in Electrical Engineering and Computer Applications, AEECA 2024

Proceedings - 2024 International Conference on Advances in E...

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5th international conference.on advances in Electrical Engineering and Computer Applications, AEECA 2024

ISBN: (纸本)9798350355253

The proceedings contain 127 papers. The topics discussed include: Advanced data storage and processing technologies in a next-generation electric information acquisition system;analyzing file access characteristics for deep learning workloads on mobile devices;optimal scheduling of distributed energy storage for electric vehicles based on evolutionary dissipation theory;a novel semi-supervised learning approach for referring expression comprehension;research and implementation of material image subject segmentation method based on machine vision;application of image recognition and 3D reconstruction technology in virtual museum system;knowledge graph technology-based active research and judgment technology for electric power customer complaint risk;and path planning for unmanned underwater vehicles based on improved ant colony algorithm.

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Third international conference.on image processing, Object Detection, and Tracking, IPODT 2024

Third International Conference on Image Processing, Object D...

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2024 3rd international conference.on image processing, Object Detection, and Tracking, IPODT 2024

ISBN: (纸本)9781510685512

The proceedings contain 35 papers. The topics discussed include: design of a book misplacement detection system based on image recognition;automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt;deep intrinsic image popularity assessment by learning to rank;a late fusion framework for multi-vehicle collaborative perception;subpixel precision BGA chip localization method based on Lagrange interpolation;signal-to-noise ratio analysis and experimental verification of imaging laser detection system;comparison of cell image segmentation results based on U-Net and transformer;and deep learning based algorithm for classifying pedestrian behavior at crosswalks.

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