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Journal articles on the topic '3D-Convolutional Neural Network (3D-CNN)'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Chang, Yang-Lang, Tan-Hsu Tan, Wei-Hong Lee, et al. "Consolidated Convolutional Neural Network for Hyperspectral Image Classification." Remote Sensing 14, no. 7 (2022): 1571. http://dx.doi.org/10.3390/rs14071571.

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The performance of hyperspectral image (HSI) classification is highly dependent on spatial and spectral information, and is heavily affected by factors such as data redundancy and insufficient spatial resolution. To overcome these challenges, many convolutional neural networks (CNN) especially 2D-CNN-based methods have been proposed for HSI classification. However, these methods produced insufficient results compared to 3D-CNN-based methods. On the other hand, the high computational complexity of the 3D-CNN-based methods is still a major concern that needs to be addressed. Therefore, this stud

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Lv, Shidong, Tao Long, Zhixian Hou, Liang Yan, and Zhenzhong Li. "3D CNN Hardware Circuit for Motion Recognition Based on FPGA." Journal of Physics: Conference Series 2363, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2363/1/012030.

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In recent years, three-dimensional convolutional neural network (3D CNN) has been widely used in the fields of action recognition and video analysis. The general purpose processors are difficult to achieve efficient and intensive computing, and the deployment of 3D CNN based on FPGA has the advantages of low power consumption, high energy efficiency, and customizability, and has gradually become a hot choice for deploying convolutional neural networks in many embedded scenarios. This paper designs a small 3D convolutional neural network based on the classic 3D convolutional neural network C3D,

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Yin, Junjie, Ningning Huang, Jing Tang, and Meie Fang. "Recognition of 3D Shapes Based on 3V-DepthPano CNN." Mathematical Problems in Engineering 2020 (January 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/7584576.

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This paper proposes a convolutional neural network (CNN) with three branches based on the three-view drawing principle and depth panorama for 3D shape recognition. The three-view drawing principle provides three key views of a 3D shape. A depth panorama contains the complete 2.5D information of each view. 3V-DepthPano CNN is a CNN system with three branches designed for depth panoramas generated from the three key views. This recognition system, i.e., 3V-DepthPano CNN, applies a three-branch convolutional neural network to aggregate the 3D shape depth panorama information into a more compact 3

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SENJAWATI, RINDU TEGAR, ESMERALDA CONTESSA DJAMAL, and FATAN KASYIDI. "Identifikasi Emosi Melalui Sinyal EEG menggunakan 3D-Convolutional Neural Network." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 2 (2024): 417. http://dx.doi.org/10.26760/elkomika.v12i2.417.

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ABSTRAKEmosi memberikan peran penting dalam interaksi manusia yang didapat melalui respon yang tepat. Respon yang tak tepat menunjukan adanya gangguan mental sehingga diperlukan identifikasi emosi. Identifikasi dapat dilakukan menggunakan aktivitas sinyal listrik di otak menggunakan Elektroensephalogram (EEG). Karena sinyal EEG pada setiap kanal merupakan urutan data maka dijadikan multi-kanal yang direpresentasikan pada matriks agar urutan-urutan data tetap terjaga. Penggunaan matriks memadukan informasi dari ketiga dimensi (kanal x frekuensi x waktu) dapat menggambarkan kompleksitas dari sin

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Jiang, Haiyang, Yaozong Pan, Jian Zhang, and Haitao Yang. "Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion." Symmetry 11, no. 6 (2019): 761. http://dx.doi.org/10.3390/sym11060761.

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In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally e

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Dong, Shidu, Zhi Liu, Huaqiu Wang, Yihao Zhang, and Shaoguo Cui. "A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1705–16. http://dx.doi.org/10.1166/jmihi.2019.2797.

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To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structu

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Avula, Sri Lasya. "Efficient 3D Medical Image Segmentation using CoTr: Bridging CNN and Transformer." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4748–54. http://dx.doi.org/10.22214/ijraset.2023.52686.

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Abstract: Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. Before CNNs, identifying objects in images was done manually using time-consuming, manual feature extraction methods. The superior performance of convolutional neural networks, when dealing with images, speech, or audio signals sets them apart from other neural networks. Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. Due to the inductive bias of locality and weight sharing inherent in convolutional operations, these

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Hu, Jinlong, Yuezhen Kuang, Bin Liao, Lijie Cao, Shoubin Dong, and Ping Li. "A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification." Computational Intelligence and Neuroscience 2019 (December 31, 2019): 1–9. http://dx.doi.org/10.1155/2019/5065214.

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Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely use

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Chen, Jiangcheng, Sheng Bi, George Zhang, and Guangzhong Cao. "High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network." Sensors 20, no. 4 (2020): 1201. http://dx.doi.org/10.3390/s20041201.

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High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning ho

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Polat, Huseyin, and Homay Danaei Mehr. "Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture." Applied Sciences 9, no. 5 (2019): 940. http://dx.doi.org/10.3390/app9050940.

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Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the pro

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Xu, Weiting, Xingcheng Han, Yingliang Zhao, et al. "Research on Underwater Acoustic Target Recognition Based on a 3D Fusion Feature Joint Neural Network." Journal of Marine Science and Engineering 12, no. 11 (2024): 2063. http://dx.doi.org/10.3390/jmse12112063.

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In the context of a complex marine environment, extracting and recognizing underwater acoustic target features using ship-radiated noise present significant challenges. This paper proposes a novel deep neural network model for underwater target recognition, which integrates 3D Mel frequency cepstral coefficients (3D-MFCC) and 3D Mel features derived from ship audio signals as inputs. The model employs a serial architecture that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) network. It replaces the traditional CNN with a multi-scale depthwise separable convo

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Mu, Guo, and Liu. "A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification." Remote Sensing 12, no. 1 (2020): 125. http://dx.doi.org/10.3390/rs12010125.

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Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborh

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Lu, Xiaofei, and Shouwang Li. "Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network." Advances in Multimedia 2024 (January 12, 2024): 1–12. http://dx.doi.org/10.1155/2024/5528497.

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As virtual reality technology advances, 3D environment design and modeling have garnered increasing attention. Applications in networked virtual environments span urban planning, industrial design, and manufacturing, among other fields. However, existing 3D modeling methods exhibit high reconstruction error precision, limiting their practicality in many domains, particularly environmental design. To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN)

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Nayak, Omprakash, Hrishikesh Khandare, Nikhil Kumar Parida, Ramnivas Giri, Rekh Ram Janghel, and Himanshu Govil. "Hyperspectral Image Classification using Hybrid Deep Convolutional Neural Network." Journal of Physics: Conference Series 2273, no. 1 (2022): 012028. http://dx.doi.org/10.1088/1742-6596/2273/1/012028.

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Abstract The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it

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Dong, Min, Zhenglin Fang, Yongfa Li, Sheng Bi, and Jiangcheng Chen. "AR3D: Attention Residual 3D Network for Human Action Recognition." Sensors 21, no. 5 (2021): 1656. http://dx.doi.org/10.3390/s21051656.

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At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN’s temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer

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Ruan, Kun, Shun Zhao, Xueqin Jiang, et al. "A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method." Applied Sciences 12, no. 10 (2022): 4886. http://dx.doi.org/10.3390/app12104886.

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Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we prepro

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Yu, Run, Youqing Luo, Haonan Li, et al. "Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images." Remote Sensing 13, no. 20 (2021): 4065. http://dx.doi.org/10.3390/rs13204065.

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As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the t

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Liu, Lu, and Guobao Feng. "Polarimetric SAR image classification using 3D generative adversarial network." MATEC Web of Conferences 336 (2021): 08012. http://dx.doi.org/10.1051/matecconf/202133608012.

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In this paper, a new architecture of three-dimensional deep convolutional generative adversarial network(3D-DCGAN) is specially defined to solve the unstable training problem of GAN and make full use of the information involved in polarimetric data. Firstly, a data cube with nine components of polarimetric coherency matrix are directly used as the input features of DCGAN. After that, a 3D convolutional model is designed as the components of generator and discriminator to construct the 3D-DCGAN, which considers the effective feature extraction capability of 3D convolutional neural network(CNN).

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Chen, Dong, and Young Hoon Joo. "A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks." Sensors 20, no. 10 (2020): 2761. http://dx.doi.org/10.3390/s20102761.

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This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in

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Ding, Bo, Lei Tang, and Yong-jun He. "An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network." Complexity 2020 (June 11, 2020): 1–14. http://dx.doi.org/10.1155/2020/9050459.

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Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input i

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Kharrat, Ahmed, and Mahmoud Neji. "Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa4.

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We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our

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Manimegalai, P., R. Suresh Kumar, Prajoona Valsalan, R. Dhanagopal, P. T. Vasanth Raj, and Jerome Christhudass. "3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine." Scanning 2022 (July 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/9640177.

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Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and machine learning (ML), which have driven the development of new AI abilities in the field. ANNs are used in both deep learning and machine learning in nuclear medicine. Alternatively, if 3D convolutional neural network (CNN) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs. In nuclear medicine, artificial intelligence reimagines and reengineers the field’s therapeutic and scientific capabilities. Und

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TAŞPINAR, Gürcan, and Nalan ÖZKURT. "3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes." Deu Muhendislik Fakultesi Fen ve Muhendislik 25, no. 73 (2023): 1–8. http://dx.doi.org/10.21205/deufmd.2023257301.

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Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders and it is threatening especially to the academic performance of children. Its neurobiological diagnosis is essential for clinicians to treat ADHD patients properly. Along with machine learning algorithms, and neuroimaging technologies, especially functional magnetic resonance imaging is increasingly used as biomarker in attention deficit hyperactivity disorder. Also, machine learning methods have been becoming popular at last times. This study presents an optimized 3-dimensional convolutional neur

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Wang, Yu, Shuyang Ma, and Xuanjing Shen. "A Novel Video Face Verification Algorithm Based on TPLBP and the 3D Siamese-CNN." Electronics 8, no. 12 (2019): 1544. http://dx.doi.org/10.3390/electronics8121544.

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In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-patch local binary pattern (TPLBP) and the 3D Siamese convolutional neural network is proposed in this paper. The proposed method takes the TPLBP texture feature which has excellent performance in face analysis as the input of the network. In order to extract the inter-frame information of the video, the texture feature maps of the multi-frames are st

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Kiranpure, Ayush. "Cyclone Intensity Prediction Using Deep Learning on INSAT-3D IR Imagery: A Comparative Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45392.

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This study investigates the effectiveness of deep learning techniques in accurately estimating tropical cyclone intensity using infrared (IR) imagery from the INSAT-3D satellite. We assess the performance of three models—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid CNN-RNN model—comparing them against traditional machine learning methods like Support Vector Machines (SVM) and Random Forests (RF). Results demonstrate that deep learning models significantly outperform traditional approaches, with the CNN-RNN model achieving the highest accuracy. These findings

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Ullah, Fath U. Min, Amin Ullah, Khan Muhammad, Ijaz Ul Haq, and Sung Wook Baik. "Violence Detection Using Spatiotemporal Features with 3D Convolutional Neural Network." Sensors 19, no. 11 (2019): 2472. http://dx.doi.org/10.3390/s19112472.

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The worldwide utilization of surveillance cameras in smart cities has enabled researchers to analyze a gigantic volume of data to ensure automatic monitoring. An enhanced security system in smart cities, schools, hospitals, and other surveillance domains is mandatory for the detection of violent or abnormal activities to avoid any casualties which could cause social, economic, and ecological damages. Automatic detection of violence for quick actions is very significant and can efficiently assist the concerned departments. In this paper, we propose a triple-staged end-to-end deep learning viole

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Gao, Zhiyong, and Jianhong Xiang. "Real-time 3D Object Detection Using Improved Convolutional Neural Network Based on Image-driven Point Cloud." (Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, no. 8 (2021): 826–36. http://dx.doi.org/10.2174/2352096514666211026142721.

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Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN comprises the frustum sequence module, 3D instance segmentation module SNET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module ENET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instanc

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Espinosa-Bernal, Osmar Antonio, Jesús Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, et al. "Quasi/Periodic Noise Reduction in Images Using Modified Multiresolution-Convolutional Neural Networks for 3D Object Reconstructions and Comparison with Other Convolutional Neural Network Models." Computers 13, no. 6 (2024): 145. http://dx.doi.org/10.3390/computers13060145.

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The modeling of real objects digitally is an area that has generated a high demand due to the need to obtain systems that are able to reproduce 3D objects from real objects. To this end, several techniques have been proposed to model objects in a computer, with the fringe profilometry technique being the one that has been most researched. However, this technique has the disadvantage of generating Moire noise that ends up affecting the accuracy of the final 3D reconstructed object. In order to try to obtain 3D objects as close as possible to the original object, different techniques have been d

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Chen, Boyu, Zhihao Zhang, Nian Liu, Yang Tan, Xinyu Liu, and Tong Chen. "Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition." Information 11, no. 8 (2020): 380. http://dx.doi.org/10.3390/info11080380.

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A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized conv

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Pastor, Francisco, Juan M. Gandarias, Alfonso J. García-Cerezo, and Jesús M. Gómez-de-Gabriel. "Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation." Sensors 19, no. 24 (2019): 5356. http://dx.doi.org/10.3390/s19245356.

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In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-relea

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Yuan, Q., Y. Ang, and H. Z. M. Shafri. "HYPERSPECTRAL IMAGE CLASSIFICATION USING RESIDUAL 2D AND 3D CONVOLUTIONAL NEURAL NETWORK JOINT ATTENTION MODEL." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 187–93. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-187-2021.

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Abstract. Hyperspectral image classification (HSIC) is a challenging task in remote sensing data analysis, which has been applied in many domains for better identification and inspection of the earth surface by extracting spectral and spatial information. The combination of abundant spectral features and accurate spatial information can improve classification accuracy. However, many traditional methods are based on handcrafted features, which brings difficulties for multi-classification tasks due to spectral intra-class heterogeneity and similarity of inter-class. The deep learning algorithm,

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Mayasari, Dita Ayu, Ihtifazhuddin Hawari, Sheba Atma Dwiyanti, et al. "Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3208. http://dx.doi.org/10.11591/ijece.v14i3.pp3208-3219.

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<span lang="EN-US">The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume data of clavi

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Ayu, Mayasari Dita, Ihtifazhuddin Hawari, Dwiyanti Sheba Atma, et al. "Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs." Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs 14, no. 3 (2024): 3208–19. https://doi.org/10.11591/ijece.v14i3.pp3208-3219.

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The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume dat

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Adhithyaa, N., A. Tamilarasi, D. Sivabalaselvamani, and L. Rahunathan. "Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network." Information Technology and Control 52, no. 3 (2023): 713–30. http://dx.doi.org/10.5755/j01.itc.52.3.33719.

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Accidents due to driver drowsiness are observed to be increasing at an alarming rate across all countries and it becomes necessary to identify driver drowsiness to reduce accident rates. Researchers handled many machine learning and deep learning techniques especially many CNN variants created for drowsiness detection, but it is dangerous to use in real time, as the design fails due to high computational complexity, low evaluation accuracies and low reliability. In this article, we introduce a multistage adaptive 3D-CNN model with multi-expressive features for Driver Drowsiness Detection (DDD)

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Gao, Junlong, and Yucheng Wei. "Depression Level Assessment based on 3D CNN and Facial Expression Videos." Advances in Engineering Technology Research 10, no. 1 (2024): 382. http://dx.doi.org/10.56028/aetr.10.1.382.2024.

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Depression has a severe impact on people's daily lives and work, and it may even lead to suicide. Computer visionbased methods are promising for providing more effective and objective assistance in the clinical diagnosis of depression. In this article, to compare the performance of different 3D convolutional neural networks in assessing depression levels, we tested 3D VGGNet18, 3D GoogleNet, 3DEfficientNetB7, and 3D MobileNetV3 networks based on the AVEC2013 and AVEC2014 datasets. Experimental results showed that the 3D MobileNetV3 network achieved the best evaluation results, with MAE=7.35 an

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Pagès, Guillaume, Benoit Charmettant, and Sergei Grudinin. "Protein model quality assessment using 3D oriented convolutional neural networks." Bioinformatics 35, no. 18 (2019): 3313–19. http://dx.doi.org/10.1093/bioinformatics/btz122.

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Abstract Motivation Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional

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Jiao, Bo. "Application Analysis of Virtual Simulation Network Model Based on 3D-CNN in Animation Teaching." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 4 (2024): 11–24. http://dx.doi.org/10.58346/jowua.2024.i4.002.

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With the continuous progress of educational technology, the application of animation in teaching has gradually become an effective means to improve learning experience and effectiveness. This study focuses on the application of 3D-CNN virtual simulation network model in animation teaching, aiming to deeply analyze the impact of this model on the learning process and its potential advantages in improving learning effectiveness. 3D-CNN stands for Three-Dimensional Convolutional Neural Network. Unlike traditional 2D-CNN, 3D-CNN is specifically designed for processing 3D data, such as video and vo

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Alnaim, Norah, Maysam Abbod, and Rafiq Swash. "Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks." Technologies 8, no. 2 (2020): 19. http://dx.doi.org/10.3390/technologies8020019.

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The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on executi

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Rao, Chengping, and Yang Liu. "Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization." Computational Materials Science 184 (November 2020): 109850. http://dx.doi.org/10.1016/j.commatsci.2020.109850.

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Kim, Harin, Sung Woo Joo, Yeon Ho Joo, and Jungsun Lee. "S152. DIAGNOSTIC CLASSIFICATION OF SCHIZOPHRENIA USING 3D CONVOLUTIONAL NEURAL NETWORK WITH RESTING-STATE FUNCTIONAL MRI." Schizophrenia Bulletin 46, Supplement_1 (2020): S94. http://dx.doi.org/10.1093/schbul/sbaa031.218.

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Abstract Background Several machine-learning (ML) algorithms have been deployed in the diagnostic classification of schizophrenia. Compared to other ML methods, the 3D convolutional neural network (CNN) has an advantage of learning complex and subtle patterns in data and preserving spatial information, which is a more suitable tool for brain imaging data. Although resting-state functional MRI (rsfMRI) data has been used in previous ML studies relating to the diagnostic classification of schizophrenia, a limited number of studies have been conducted using resting-state functional connectivity r

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Jain, Nidhi, and Prasadu Peddi. "Facial Expression Identification using Two-Stream Convolutional Neural Networks (TSCNNs) and Inception 3D Convolutional Neural Network (CNN)." International Journal of Renewable Energy Exchange 11, no. 5 (2023): 113–22. http://dx.doi.org/10.58443/ijrex.11.5.2023.113-122.

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Bashi, Omar I. Dallal, Husamuldeen K. Hameed, Yasir Mahmood Al Kubaiaisi, and Ahmad H. Sabry. "Development of object detection from point clouds of a 3D dataset by Point-Pillars neural network." Eastern-European Journal of Enterprise Technologies 2, no. 9 (122) (2023): 26–33. http://dx.doi.org/10.15587/1729-4061.2023.275155.

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Deep learning algorithms are able to automatically handle point clouds over a broad range of 3D imaging implementations. They have applications in advanced driver assistance systems, perception and robot navigation, scene classification, surveillance, stereo vision, and depth estimation. According to prior studies, the detection of objects from point clouds of a 3D dataset with acceptable accuracy is still a challenging task. The Point-Pillars technique is used in this work to detect a 3D object employing 2D convolutional neural network (CNN) layers. Point-Pillars architecture includes a learn

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Omar, I. Dallal Bashi, K. Hameed Husamuldeen, Mahmood Al Kubaiaisi Yasir, and H. Sabry Ahmad. "Development of object detection from point clouds of a 3D dataset by Point-Pillars neural network." Eastern-European Journal of Enterprise Technologies 2, no. 9(122) (2023): 26–33. https://doi.org/10.15587/1729-4061.2023.275155.

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Deep learning algorithms are able to automatically handle point clouds over a broad range of 3D imaging implementations. They have applications in advanced driver assistance systems, perception and robot navigation, scene classification, surveillance, stereo vision, and depth estimation. According to prior studies, the detection of objects from point clouds of a 3D dataset with acceptable accuracy is still a challenging task. The Point-Pillars technique is used in this work to detect a 3D object employing 2D convolutional neural network (CNN) layers. Point-Pillars architecture includes a learn

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Kim, Sungkyu, Tae-Seong Kim, and Won Hee Lee. "Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition." Sensors 22, no. 18 (2022): 6813. http://dx.doi.org/10.3390/s22186813.

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Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the type of input features. Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity. Here, we propose a novel 3D convolutional neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition, with the aim of accelerating the CN

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Wang, Haiying. "Recognition of Wrong Sports Movements Based on Deep Neural Network." Revue d'Intelligence Artificielle 34, no. 5 (2020): 663–71. http://dx.doi.org/10.18280/ria.340518.

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During physical education (PE), the teaching quality is severely affected by problems like nonstandard technical movements or wrong demonstrative movements. High-speed photography can capture instantaneous movements that cannot be recognized with naked eyes. Therefore, this technology has been widely used to judge the sprint movements in track and field competitions, and assess the quality of artistic gymnastics. Inspired by three-dimensional (3D) image analysis, this paper proposes a method to recognize the standard and wrong demonstrative sports movements, based on 3D convolutional neural ne

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Cui, Liyuan, Shanhua Han, Shouliang Qi, Yang Duan, Yan Kang, and Yu Luo. "Deep symmetric three-dimensional convolutional neural networks for identifying acute ischemic stroke via diffusion-weighted images." Journal of X-Ray Science and Technology 29, no. 4 (2021): 551–66. http://dx.doi.org/10.3233/xst-210861.

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BACKGROUND: Acute ischemic stroke (AIS) results in high morbidity, disability, and mortality. Early and automatic diagnosis of AIS can help clinicians administer the appropriate interventions. OBJECTIVE: To develop a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) for automated AIS diagnosis via diffusion-weighted imaging (DWI) images. METHODS: This study includes 190 study subjects (97 AIS and 93 Non-AIS) by collecting both DWI and Apparent Diffusion Coefficient (ADC) images. 3D DWI brain images are split into left and right hemispheres and input into two paths. A map with 125

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Suryakanth, B., and S. A. Hari Prasad. "3D CNN-Residual Neural Network Based Multimodal Medical Image Classification." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (October 31, 2022): 204–14. http://dx.doi.org/10.37394/23208.2022.19.22.

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Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method use

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Al-Khuzaie, Maryam I. Mousa, and Waleed A. Mahmoud Al-Jawher. "Enhancing Brain Tumor Classification with a Novel Three-Dimensional Convolutional Neural Network (3D-CNN) Fusion Model." Journal Port Science Research 7, no. 3 (2024): 254–67. http://dx.doi.org/10.36371/port.2024.3.5.

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Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze brain tumour images (BT) to understand the disease's progress better. It is well-known that training 3D-CNN is computationally expensive and has the potential of overfitting due to the small sample size available in the medical imaging field. Here, we proposed a novel 2D-3D approach by converting a 2D brain image to a 3D fused image using a gradient of the image Learnable Weighted. By the 2D-to-3D conversion, the proposed model can easily forward the fused 3D image through a pre-trained 3D model while

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Qing, Yuhao, and Wenyi Liu. "Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism." Remote Sensing 13, no. 3 (2021): 335. http://dx.doi.org/10.3390/rs13030335.

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In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a princ

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Wagner, F., and H. G. Maas. "A COMPARATIVE STUDY OF DEEP ARCHITECTURES FOR VOXEL SEGMENTATION IN VOLUME IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1667–76. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1667-2023.

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Abstract. This study investigates the performance of eight different deep learning architectures for voxel segmentation in volume images. The motivation is to segment carbon in carbon reinforced concrete (CRC) in micro-tomography (μ-CT) data. Although there are many 3D convolutional neural networks (CNNs) available, it is not yet clear which one works best for these specific tasks. In this study, the authors compare the following networks: DenseVoxNet, HighResNet, Med3D, Residual 3D U-Net, 3D SkipDenseSeg, 3D U-Net, V-Net, and LV-Net. To provide a more general recommendation for selecting a ne

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