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检索条件"任意字段=3rd International Conference on 3-D Digital Imaging and Modeling"

共 1766 条记录，以下是21-30 订阅

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Lung cancer detection using convolutional neural network and transfer learning

Lung cancer detection using convolutional neural network and...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Rahul Sharma Akanksha dubey Manoj diwakar Shagun Gupta Upendra Pratap Singh Monika Kumari Nath D. Y. Patil International University Pune India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Lung cancer has emerged as a significant cause for concern among individuals worldwide. Consequently, several nations provide financial resources and extend invitations to researchers and medical professionals to collaborate in addressing this issue. Lung cancer is a significant contributor to mortality subsequent to heart illness. The timely identification of lung cancer in its first stages has the potential to enhance patient survival rates. The use of Computer Vision is of utmost importance in the timely identification of lung cancer and the implementation of early therapeutic interventions, hence enhancing the likelihood of patient survival. The proposed deep learning model incorporates Convolutional Neural Network (CNN) and transfer learning techniques, along with other image processing methods like image compression and picture enhancement. These approaches are used to analyze medical images and make correct predictions. Our study demonstrates a CNN model accuracy of 91.29% and a transfer learning accuracy of 95.9%.

关键词： deep learning Computer vision Accuracy Image coding Computational modeling Transfer learning Lung cancer Feature extraction Convolutional neural networks Biomedical imaging

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Automated Brain Tumor Identification in MRI Scans Using State-of-the-Art deep Learning Models

Automated Brain Tumor Identification in MRI Scans Using Stat...

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disruptive Technologies (ICdT), international conference on

作者： Sachin Kumar Ayushi dewan Surabhi Solanki Janhvi d. dave Praveen Kumar Patidar Shivkumar Lilhare Department of CSE Manav Rachna International Institute of Research and Studies Faridabad India School of CSE Lovely Professional University Phagwara India SCSET Bennett University Greater Noida India Department of CSE Parul University Vadodara India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A lump or development of aberrant cells in your brain that grows unhindered is called a brain tumour. Cancer or tumours may begin in the brain or spread there from other parts of the body. Early brain cancer detection is an extremely difficult task for radiologists. Brain tumours grow quickly; they often double in size in 25 days. Patients who receive inadequate treatment typically have a survival rate of no more than six months. It can quickly result in death. Because of this, an automated approach is necessary to detect brain tumours at an early stage. This idea suggests an automated technique for quickly distinguishing between a cancer and a healthy brain using the brain's magnetic resonance imaging (MRI). Convolutional Neural Networks (CNN), two deep learning principles, are used in this research. Two models-one with two convolutional layers and the other with two fully linked layers-are displayed in this research. And the second model, which has three fully, linked layers and four convolutional layers. When testing these models, satisfactory accuracy was attained; hence, radiologists can use this method in real life to promptly identify cancers and take action that could potentially save lives.

关键词： deep learning Accuracy Magnetic resonance imaging Predictive models Brain modeling data models Planning Convolutional neural networks Tumors Testing

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Leveraging Transfer Learning and Image Processing for Brain Tumor Classification from MRI data

Leveraging Transfer Learning and Image Processing for Brain ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Seerat Singla Rupesh Gupta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

This paper presents a deep learning approach for the classification of brain tumors using MRI images, leveraging the Xception architecture for efficient and accurate detection. The dataset consisted of images related to brain tumors and healthy ones. It was preprocessed and image enhancement was done on it to improve the performance of the model. The network that was fine-tuned with transfer learning resulted in a test accuracy of about 0.989 with minor loss, indicating its good discriminative capability between the case of brain tumor and the healthy one. Evaluation of the performance by confusion matrix analysis indicates high sensitivity and specificity levels with just minor false positives and false negatives. The proposed model shows tremendous potential for real-world medical applications as it offers an automated accurate, and efficient capability of detecting the existence of brain tumors. This work provides a basis for further improvements that come in exploring different types of datasets as well as using 3d MRI scans to further improve the strength of the system. It is shown from these results that deep learning is viable as the next frontier toward improved brain tumor diagnosis: hence, more promise by this model, in early detection and subsequent reduction of workloads at diagnostic levels within clinical practice.

关键词： deep learning Accuracy Three-dimensional displays Magnetic resonance imaging Transfer learning Brain tumors Sensitivity and specificity Brain modeling data models Image classification

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Structure-Preserving 3d Reconstruction Algorithm for Traditional Buildings Based on Hybrid imaging Techniques

Structure-Preserving 3D Reconstruction Algorithm for Traditi...

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international conference on Inventive Computation Technologies (ICICT)

作者： Guangpei Yang Yue Zhong School of Architectural Engineering and Art Guangzhou Nanyang Polytechnic Guangzhou Guangdong China School of Information Science and Technology Guangdong Construction Polytechnic Guangzhou Guangdong China

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This study proposes a novel traditional building structure-preserving 3d reconstruction algorithm based on hybrid imaging technology. The main application scenario of this algorithm is to provide a digital solution for building protection for the deformation and degradation of the traditional buildings caused by natural or human factors. This study first analyzes RGB imaging, thermal imaging, and 3d point cloud as three mainstream imaging technologies, and conducts a preliminary analysis of different modes of image analysis. At the algorithm design level, this study first uses the cross-modal registration algorithm, SIFT feature extraction and neural network density optimization method for comprehensive modeling. This operation achieves accurate alignment of RGB images, thermal imaging images and 3d point cloud data. Then, in the image detail optimization stage. This study uses high-frequency detail enhancement technology based on wavelet transform to enhance details. This operation enhances the texture and edge information of the image. Finally, combined with the partial completion algorithm of the image style transfer. Local style transfer is performed on the target area under the style prior background, thereby completing the restoration and reconstruction of the architectural image. Experimental results show that the proposed algorithm is superior to the traditional algorithm in image restoration quality, and obtains good performance in specific indicators, such as PSNR value of 32.0 dB, SSIM value of 0.85, and LPIPS value of 0.08.

关键词： Point cloud compression Wavelet transforms Three-dimensional displays Accuracy Image edge detection Buildings imaging Thermal conductivity Image restoration Image reconstruction

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Investigation on Enhanced detection of Parkinson's disease in Brain imaging Using Ensemble Learning Approaches

Investigation on Enhanced Detection of Parkinson's Disease i...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： S. Sathishkumar S. P. Santhoshkumar Satyam Kumar N. Banupriya S. Hariharasudhan S. Gajalakshmi Department of Computer Science and Engineering Hindustan Institute of Technology Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India School of CS and IT Mahatma Gandhi Central University Motihari India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Masses of people around the world are affected by Parkinson's disease (Pd), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve Pd diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (dT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS.

关键词： Neuroimaging Support vector machines Accuracy Parkinson's disease imaging Sensitivity and specificity Brain modeling Feature extraction Ensemble learning Resilience

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Progressing Alzheimer's diagnosis with Ensemble CNN Model

Progressing Alzheimer's Diagnosis with Ensemble CNN Model

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disruptive Technologies (ICdT), international conference on

作者： Vivek Yadav Vaibhav Rohit Sushil Kumar Umang Rastogi CSE Department KIET Group of Institutions Ghaziabad India CSE Department Data Science & Deep Learning (DSDL) Lab KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper proposes an Alzheimer's disease prediction model using CNN, implemented on a system with AMd Radeon Vega 8 Graphics to improve computational traceability. The research is encouraging because the implicates of the model touched the 97% accuracy of the disease prediction by MRI images of patients. The integration of the Vega 8 GPU allows the system to process large datasets and perform computations at a significantly faster rate, thereby reducing training time. The MRI dataset used in this study is highly imbalanced, with four classes: Non-demented or Non-d, Very Mild dementia or V.M.d, Mild dementia or M.d, Moderate demented or M.d.A. CNN was chosen for its capability in highlight extraction and learning from MRI pictures, without requiring human interference. The show was run with parallel and multiclass datasets, and IT got 97% exactness. The systematic analysis of the test shows that the system can diagnose Alzheimer's right at the initial stage of the disease development. This approach shows how CNNs with the aid of AMd Radeon Vega 8 Graphics perform well in automating Alzheimer's diagnosis while breaking the barriers associated with manual feature extraction and orthodox dependence on experts.

关键词： Training deep learning Accuracy Magnetic resonance imaging Computational modeling Transfer learning Graphics processing units Computer architecture Predictive models Alzheimer's disease

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Proceedings - 2nd international conference on 3-d digital imaging and modeling, 3dIM 1999

Proceedings - 2nd International Conference on 3-D Digital Im...

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2nd international conference on 3-d digital imaging and modeling, 3dIM 1999

ISBN: (纸本)0769500625

The proceedings contain 57 papers. The topics discussed include: A low-cost range finder using a visually located, structured light source;digital 3d imaging system for rapid response on remote sites;advances in the cooperation of shape from shading and stereo vision;locking onto 3d-structure by a combined vergence and fusion system;3d shape recovery and registration based on the projection of non coherent structured light;curve and surface models to drive 3d reconstruction using stereo and shading;computing consistent normals and colors from photometric data;Bayesian estimation of distance and surface normal with a time-of-flight laser rangefinder;and model-based scanning path generation for inspection.

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3rd international conference on 3d imaging Technologies- Multidimensional Signal Processing and deep Learning, 3dIT-MSP and dL 2021

3rd International Conference on 3D Imaging Technologies- Mul...

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3rd international conference on 3d imaging Technologies- Multidimensional Signal Processing and deep Learning, 3dIT-MSP and dL 2021

ISBN: (纸本)9789811924477

The proceedings contain 24 papers. The special focus in this conference is on 3d imaging Technologies- Multidimensional Signal Processing and deep Learning. The topics include: Grid False data Intrusion detection Method Based on Edge Computing and Federated Learning;comparative Analysis of Automatic Poetry Generation Systems Based on different Recurrent Neural Networks;citrus Positioning Method Based on Camera and Lidar data Fusion;network Intrusion detection Based on Apriori-Kmeans Algorithm;research on the Adaptive Matching Mechanism of Graphic design Elements Based on Visual Communication Technology;research on Graphic design of digital Media Art Based on Computer Aided Algorithm;water Environmental Quality Assessment and Effect Prediction Based on Artificial Neural Network;full-Focus imaging detection of Ship Ultrasonic-Phased Array Based on directivity Function;research on Visual Communication of Graphic design Based on Machine Vision;dynamic Grey Wolf Optimization Algorithm Based on Quasi-Opposition Learning;innovative design of Traditional Arts and Crafts Based on 3d digital Technology;Insulator detection Study Based on Improved Faster-RCNN;image-Based Physics Rendering for 3d Surface Reconstruction: A Survey;Global Analysis of discrete SIR and SIS Systems;a Fast Heuristic k-means Algorithm Based on Nearest Neighbor Information;Color Restoration of RGB-NIR Images in Low-Light Environment Using CycleGAN;preface;longitudinal Structure Analysis and Segmentation Algorithm of dongba document;development of Mobile Food Recognition System Based on deep Convolutional Network;a Novel Space division Rough Set Model for Feature Selection;Overview of SAR Image Change detection Based on Segmentation;image Recognition Methods Based on deep Learning.

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Proceedings - 5th international conference on 3-d digital imaging and modeling, 3dIM 2005

Proceedings - 5th International Conference on 3-D Digital Im...

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5th international conference on 3-d digital imaging and modeling, 3dIM 2005

ISBN: (纸本)0769523277

The proceedings contain 74 papers. The topics discussed include: further improving geometric fitting;a light modulation/demodulation method for real-time 3d imaging;a MRF formulation for coded structured light;partial surface integration based on variational implicit functions and surfaces for 3d model building;multiresolution interactive modeling with efficient visualization;multisensor fusion for volumetric reconstruction of large outdoor areas;unsupervised 3d object recognition and reconstruction in unordered datasets;digitizing archaeological excavations from multiple views;an improved calibration technique for coupled single-row telemeter and CCd camera;incremental Catmull-Clark subdivision;fitting of 3d circles and ellipses using a parameter decomposition approach;and an efficient scattered data approximation using multilevel B-splines based on quasi-interpolants.

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3rd international conference on 3d imaging Technologies - Multidimensional Signal Processing and deep Learning, 3dIT-MSP and dL 2021

3rd International conference on 3D Imaging Technologies - Mu...

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3rd international conference on 3d imaging Technologies - Multidimensional Signal Processing and deep Learning, 3dIT-MSP and dL 2021

ISBN: (纸本)9789811924514

The proceedings contain 23 papers. The special focus in this conference is on 3d imaging Technologies - Multidimensional Signal Processing and deep Learning. The topics include: Prediction of Silicon Content in Molten Iron Based on EMd-GA-LSTM;using Meta Path Information to Adjust Embedded Recommendation System;intelligent Campus Blog Preprocessing Model;an Experimental Study on Extraction Method of Tobacco Color distribution Feature;research on the distribution Map of Weeds in Rice Field Based on SegNet;preface;study on Remote Sensing Retrieval of Land Surface Evapotranspiration in Poyang Lake Region in Typical Hydrological Year;research on the Application of Artificial Intelligence in Unstructured data Resource Management of Power News Information;analysis and Application of Medical Images in the Field of Artificial Intelligence;development and design of Surface Quality Online Inspection System Based on Machine Vision;terahertz Surface-Plasmon-Polaritons Gradient Index Lens;Improvement of YOLOv4 Algorithm and Its Application in Objective Identification of Railway Level Crossing;design of Automatic Verification System for Electronic Balance Based on Machine Vision;research on Interactive Art design System Based on Artificial Intelligence Technology;infrared imaging Circuit Multi-Channel Analog-To-digital Sampling Circuit Inconsistency Correction;research on Artificial Intelligence System design of Ideological and Political Education;research on 3d Virtual Emotional Interaction design Based on Network digitization;research on Image Quality Assessment of Vehicle Panoramic View Equipment;research and Implementation of digital Art Media System Based on Big data Aesthetics;Research on Application of OpenGL-Based Game design;application of Artificial Intelligence Technology in Marketing.

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